QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(1):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.cx(input_qubit[1],input_qubit[2]) # number=38\\n prog.h(input_qubit[3]) # number=8\\n\\n\\n prog.h(input_qubit[0]) # number=39\\n prog.cz(input_qubit[1],input_qubit[0]) # number=40\\n prog.h(input_qubit[0]) # number=41\\n prog.h(input_qubit[0]) # number=31\\n prog.cz(input_qubit[1],input_qubit[0]) # number=32\\n prog.h(input_qubit[0]) # number=33\\n prog.x(input_qubit[0]) # number=29\\n prog.cx(input_qubit[1],input_qubit[0]) # number=30\\n prog.h(input_qubit[0]) # number=34\\n prog.cz(input_qubit[1],input_qubit[0]) # number=35\\n prog.h(input_qubit[0]) # number=36\\n prog.x(input_qubit[1]) # number=10\\n prog.cx(input_qubit[0],input_qubit[2]) # number=25\\n prog.x(input_qubit[2]) # number=26\\n prog.cx(input_qubit[0],input_qubit[2]) # number=27\\n prog.x(input_qubit[3]) # number=12\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.x(input_qubit[0]) # number=13\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[4]) # number=42\\n prog.cz(input_qubit[2],input_qubit[4]) # number=43\\n prog.h(input_qubit[4]) # number=44\\n prog.h(input_qubit[3]) # number=20\\n\\n prog.h(input_qubit[0]) \\n prog.h(input_qubit[1])\\n prog.h(input_qubit[2])\\n prog.h(input_qubit[3])\\n\\n\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n backend = FakeVigo()\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy954.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n# @Time : 5/15/20 4:49 PM\\n# @File : grover.py\\n\\n# qubit number=4\\n# total number=17\\nimport cirq\\nimport cirq.google as cg\\nfrom typing import Optional\\nimport sys\\nfrom math import log2\\nimport numpy as np\\n\\n#thatsNoCode\\n\\ndef make_circuit(n: int, input_qubit):\\n c = cirq.Circuit() # circuit begin\\n\\n c.append(cirq.H.on(input_qubit[0])) # number=1\\n c.append(cirq.H.on(input_qubit[1])) # number=2\\n c.append(cirq.H.on(input_qubit[1])) # number=7\\n c.append(cirq.H.on(input_qubit[2])) # number=3\\n c.append(cirq.H.on(input_qubit[3])) # number=4\\n c.append(cirq.CNOT.on(input_qubit[3],input_qubit[0])) # number=5\\n c.append(cirq.H.on(input_qubit[0])) # number=12\\n c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=13\\n c.append(cirq.H.on(input_qubit[0])) # number=14\\n c.append(cirq.SWAP.on(input_qubit[1],input_qubit[0])) # number=8\\n c.append(cirq.SWAP.on(input_qubit[1],input_qubit[0])) # number=9\\n c.append(cirq.SWAP.on(input_qubit[1],input_qubit[0])) # number=10\\n c.append(cirq.SWAP.on(input_qubit[1],input_qubit[0])) # number=11\\n c.append(cirq.Y.on(input_qubit[1])) # number=15\\n c.append(cirq.Y.on(input_qubit[1])) # number=16\\n # circuit end\\n\\n\\n return c\\n\\ndef bitstring(bits):\\n return ''.join(str(int(b)) for b in bits)\\n\\nif __name__ == '__main__':\\n qubit_count = 4\\n\\n input_qubits = [cirq.GridQubit(i, 0) for i in range(qubit_count)]\\n circuit = make_circuit(qubit_count,input_qubits)\\n circuit = cg.optimized_for_sycamore(circuit, optimizer_type='sqrt_iswap')\\n\\n circuit_sample_count =2820\\n\\n info = cirq.final_state_vector(circuit)\\n\\n qubits = round(log2(len(info)))\\n frequencies = {\\n np.binary_repr(i, qubits): round((info[i]*(info[i].conjugate())).real,3)\\n for i in range(2 ** qubits)\\n }\\n writefile = open(\\\"../data/startCirq_Class387.csv\\\",\\\"w+\\\")\\n\\n print(format(frequencies),file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n\\n print(circuit.__len__(), file=writefile)\\n print(circuit,file=writefile)\\n\\n\\n writefile.close()\",\n \"# qubit number=5\\n# total number=41\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(1):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[4]) # number=27\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=38\\n prog.x(input_qubit[0]) # number=39\\n prog.cx(input_qubit[1],input_qubit[0]) # number=40\\n prog.x(input_qubit[1]) # number=10\\n prog.x(input_qubit[4]) # number=34\\n prog.cx(input_qubit[0],input_qubit[2]) # number=22\\n prog.x(input_qubit[2]) # number=23\\n prog.h(input_qubit[2]) # number=28\\n prog.cz(input_qubit[0],input_qubit[2]) # number=29\\n prog.h(input_qubit[2]) # number=30\\n prog.cx(input_qubit[0],input_qubit[3]) # number=35\\n prog.x(input_qubit[3]) # number=36\\n prog.cx(input_qubit[0],input_qubit[3]) # number=37\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.x(input_qubit[0]) # number=13\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.y(input_qubit[1]) # number=26\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[2]) # number=31\\n prog.cz(input_qubit[4],input_qubit[2]) # number=32\\n prog.h(input_qubit[2]) # number=33\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n prog.h(input_qubit[0]) \\n prog.h(input_qubit[1])\\n prog.h(input_qubit[2])\\n prog.h(input_qubit[3])\\n\\n\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC933.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=10\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nimport networkx as nx\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef make_circuit(n:int) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n prog = QuantumCircuit(input_qubit)\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n\\n for edge in E:\\n k = edge[0]\\n l = edge[1]\\n prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1])\\n prog.p(gamma, k)\\n prog.p(gamma, l)\\n\\n prog.rx(2 * beta, range(len(V)))\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=5\\n prog.h(input_qubit[0]) # number=7\\n prog.cz(input_qubit[1],input_qubit[0]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n n = 4\\n V = np.arange(0, n, 1)\\n E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]\\n\\n G = nx.Graph()\\n G.add_nodes_from(V)\\n G.add_weighted_edges_from(E)\\n\\n step_size = 0.1\\n\\n a_gamma = np.arange(0, np.pi, step_size)\\n a_beta = np.arange(0, np.pi, step_size)\\n a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)\\n\\n F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (\\n 1 + np.cos(4 * a_gamma) ** 2)\\n\\n result = np.where(F1 == np.amax(F1))\\n a = list(zip(result[0], result[1]))[0]\\n\\n gamma = a[0] * step_size\\n beta = a[1] * step_size\\n\\n prog = make_circuit(4)\\n sample_shot =5200\\n writefile = open(\\\"../data/startQiskit61.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = BasicAer.get_backend('qasm_simulator')\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.measure_all()\\n prog = circuit1\\n\\n info = execute(prog,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=40\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.cx(input_qubit[0],input_qubit[3]) # number=13\\n prog.h(input_qubit[3]) # number=23\\n prog.cz(input_qubit[0],input_qubit[3]) # number=24\\n prog.y(input_qubit[1]) # number=37\\n prog.h(input_qubit[3]) # number=25\\n prog.x(input_qubit[3]) # number=18\\n prog.cx(input_qubit[0],input_qubit[3]) # number=19\\n prog.cx(input_qubit[0],input_qubit[3]) # number=15\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=12\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=32\\n prog.cx(input_qubit[3],input_qubit[0]) # number=20\\n prog.cx(input_qubit[3],input_qubit[0]) # number=26\\n prog.z(input_qubit[3]) # number=27\\n prog.h(input_qubit[0]) # number=29\\n prog.cz(input_qubit[3],input_qubit[0]) # number=30\\n prog.h(input_qubit[0]) # number=31\\n prog.h(input_qubit[0]) # number=33\\n prog.cz(input_qubit[3],input_qubit[0]) # number=34\\n prog.h(input_qubit[0]) # number=35\\n prog.h(input_qubit[2]) # number=36\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[2]) # number=10\\n prog.y(input_qubit[2]) # number=11\\n prog.y(input_qubit[1]) # number=38\\n prog.y(input_qubit[1]) # number=39\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC2807.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=2\\n# total number=21\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import IBMQ\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename='circuit/deutsch-oracle.png')\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n\\n input_qubit = QuantumRegister(n, \\\"qc\\\")\\n target = QuantumRegister(1, \\\"qt\\\")\\n prog = QuantumCircuit(input_qubit, target)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(target)\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n\\n prog.h(input_qubit[1]) # number=1\\n prog.h(target)\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [target])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n #for i in range(n):\\n # prog.measure(input_qubit[i], classicals[i])\\n\\n prog.y(input_qubit[1]) # number=2\\n prog.y(input_qubit[1]) # number=4\\n prog.y(input_qubit[1]) # number=3\\n prog.h(input_qubit[0]) # number=13\\n prog.cz(input_qubit[1],input_qubit[0]) # number=14\\n prog.h(input_qubit[0]) # number=15\\n prog.cx(input_qubit[1],input_qubit[0]) # number=18\\n prog.x(input_qubit[0]) # number=19\\n prog.cx(input_qubit[1],input_qubit[0]) # number=20\\n prog.cx(input_qubit[1],input_qubit[0]) # number=9\\n prog.cx(input_qubit[1],input_qubit[0]) # number=10\\n prog.x(input_qubit[0]) # number=11\\n prog.cx(input_qubit[1],input_qubit[0]) # number=12\\n prog.x(input_qubit[0]) # number=16\\n prog.x(input_qubit[0]) # number=17\\n # circuit end\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n n = 2\\n f = lambda rep: rep[-1]\\n # f = lambda rep: \\\"1\\\" if rep[0:2] == \\\"01\\\" or rep[0:2] == \\\"10\\\" else \\\"0\\\"\\n # f = lambda rep: \\\"0\\\"\\n prog = make_circuit(n, f)\\n sample_shot =2800\\n backend = BasicAer.get_backend('qasm_simulator')\\n\\n circuit1 = transpile(prog,FakeVigo())\\n circuit1.x(qubit=3)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n prog = circuit1\\n\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n\\n writefile = open(\\\"../data/startQiskit365.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=46\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=23\\n prog.rx(-0.6848671984825748,input_qubit[1]) # number=26\\n prog.cz(input_qubit[0],input_qubit[3]) # number=24\\n prog.h(input_qubit[3]) # number=25\\n prog.h(input_qubit[3]) # number=37\\n prog.cz(input_qubit[0],input_qubit[3]) # number=38\\n prog.h(input_qubit[3]) # number=39\\n prog.cx(input_qubit[0],input_qubit[3]) # number=30\\n prog.cx(input_qubit[0],input_qubit[3]) # number=40\\n prog.x(input_qubit[3]) # number=41\\n prog.cx(input_qubit[0],input_qubit[3]) # number=42\\n prog.cx(input_qubit[0],input_qubit[3]) # number=32\\n prog.h(input_qubit[3]) # number=33\\n prog.cz(input_qubit[0],input_qubit[3]) # number=34\\n prog.h(input_qubit[3]) # number=35\\n prog.cx(input_qubit[0],input_qubit[3]) # number=15\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=12\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.cx(input_qubit[3],input_qubit[0]) # number=20\\n prog.cx(input_qubit[3],input_qubit[0]) # number=43\\n prog.z(input_qubit[3]) # number=44\\n prog.cx(input_qubit[3],input_qubit[0]) # number=45\\n prog.h(input_qubit[0]) # number=27\\n prog.cz(input_qubit[3],input_qubit[0]) # number=28\\n prog.h(input_qubit[0]) # number=29\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[2]) # number=10\\n prog.h(input_qubit[1]) # number=36\\n prog.y(input_qubit[2]) # number=11\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC3054.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=45\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\n\\ndef build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename=(kernel + '-oracle.png'))\\n return oracle\\n\\n\\ndef build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the Bernstein-Vazirani circuit\\n zero = np.binary_repr(0, n)\\n b = f(zero)\\n\\n # initial n + 1 bits\\n input_qubit = QuantumRegister(n+1, \\\"qc\\\")\\n classicals = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classicals)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(input_qubit[n])\\n # circuit begin\\n prog.h(input_qubit[1]) # number=1\\n prog.h(input_qubit[2]) # number=38\\n prog.cz(input_qubit[0],input_qubit[2]) # number=39\\n prog.h(input_qubit[2]) # number=40\\n prog.h(input_qubit[2]) # number=42\\n prog.cz(input_qubit[0],input_qubit[2]) # number=43\\n prog.h(input_qubit[2]) # number=44\\n prog.cx(input_qubit[0],input_qubit[2]) # number=35\\n prog.x(input_qubit[2]) # number=36\\n prog.cx(input_qubit[0],input_qubit[2]) # number=37\\n prog.cx(input_qubit[0],input_qubit[2]) # number=33\\n prog.h(input_qubit[2]) # number=25\\n prog.cz(input_qubit[0],input_qubit[2]) # number=26\\n prog.h(input_qubit[2]) # number=27\\n prog.h(input_qubit[1]) # number=7\\n prog.cz(input_qubit[2],input_qubit[1]) # number=8\\n prog.rx(0.17592918860102857,input_qubit[2]) # number=34\\n prog.rx(-0.3989822670059037,input_qubit[1]) # number=30\\n prog.h(input_qubit[1]) # number=9\\n prog.h(input_qubit[1]) # number=18\\n prog.cz(input_qubit[2],input_qubit[1]) # number=19\\n prog.h(input_qubit[1]) # number=20\\n prog.y(input_qubit[1]) # number=14\\n prog.h(input_qubit[1]) # number=22\\n prog.cz(input_qubit[2],input_qubit[1]) # number=23\\n prog.h(input_qubit[1]) # number=24\\n prog.z(input_qubit[2]) # number=3\\n prog.z(input_qubit[1]) # number=41\\n prog.x(input_qubit[1]) # number=17\\n prog.y(input_qubit[2]) # number=5\\n prog.x(input_qubit[2]) # number=21\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.h(input_qubit[n])\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [input_qubit[n]])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n return prog\\n\\n\\ndef get_statevector(prog: QuantumCircuit) -> Any:\\n state_backend = Aer.get_backend('statevector_simulator')\\n statevec = execute(prog, state_backend).result()\\n quantum_state = statevec.get_statevector()\\n qubits = round(log2(len(quantum_state)))\\n quantum_state = {\\n \\\"|\\\" + np.binary_repr(i, qubits) + \\\">\\\": quantum_state[i]\\n for i in range(2 ** qubits)\\n }\\n return quantum_state\\n\\n\\ndef evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any:\\n # Q: which backend should we use?\\n\\n # get state vector\\n quantum_state = get_statevector(prog)\\n\\n # get simulate results\\n\\n # provider = IBMQ.load_account()\\n # backend = provider.get_backend(backend_str)\\n # qobj = compile(prog, backend, shots)\\n # job = backend.run(qobj)\\n # job.result()\\n backend = Aer.get_backend(backend_str)\\n # transpile/schedule -> assemble -> backend.run\\n results = execute(prog, backend, shots=shots).result()\\n counts = results.get_counts()\\n a = Counter(counts).most_common(1)[0][0][::-1]\\n\\n return {\\n \\\"measurements\\\": counts,\\n # \\\"state\\\": statevec,\\n \\\"quantum_state\\\": quantum_state,\\n \\\"a\\\": a,\\n \\\"b\\\": b\\n }\\n\\n\\ndef bernstein_test_1(rep: str):\\n \\\"\\\"\\\"011 . x + 1\\\"\\\"\\\"\\n a = \\\"011\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_2(rep: str):\\n \\\"\\\"\\\"000 . x + 0\\\"\\\"\\\"\\n a = \\\"000\\\"\\n b = \\\"0\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_3(rep: str):\\n \\\"\\\"\\\"111 . x + 1\\\"\\\"\\\"\\n a = \\\"111\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n n = 2\\n a = \\\"11\\\"\\n b = \\\"1\\\"\\n f = lambda rep: \\\\\\n bitwise_xor(bitwise_dot(a, rep), b)\\n prog = build_circuit(n, f)\\n sample_shot =4000\\n writefile = open(\\\"../data/startQiskit_noisy239.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = FakeYorktown()\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.h(qubit=2)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n\\n info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=40\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=23\\n prog.rx(-0.6848671984825748,input_qubit[1]) # number=26\\n prog.cz(input_qubit[0],input_qubit[3]) # number=24\\n prog.h(input_qubit[3]) # number=25\\n prog.cx(input_qubit[0],input_qubit[3]) # number=17\\n prog.cx(input_qubit[0],input_qubit[3]) # number=30\\n prog.x(input_qubit[3]) # number=31\\n prog.cx(input_qubit[0],input_qubit[3]) # number=32\\n prog.h(input_qubit[3]) # number=33\\n prog.cz(input_qubit[0],input_qubit[3]) # number=34\\n prog.h(input_qubit[3]) # number=35\\n prog.cx(input_qubit[0],input_qubit[3]) # number=15\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=12\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[0]) # number=37\\n prog.cz(input_qubit[3],input_qubit[0]) # number=38\\n prog.h(input_qubit[0]) # number=39\\n prog.z(input_qubit[3]) # number=21\\n prog.h(input_qubit[0]) # number=27\\n prog.cz(input_qubit[3],input_qubit[0]) # number=28\\n prog.h(input_qubit[0]) # number=29\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[2]) # number=10\\n prog.h(input_qubit[1]) # number=36\\n prog.y(input_qubit[2]) # number=11\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = BasicAer.get_backend('qasm_simulator')\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit2536.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=47\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[4]) # number=21\\n prog.cx(input_qubit[3],input_qubit[0]) # number=32\\n prog.cx(input_qubit[3],input_qubit[0]) # number=44\\n prog.z(input_qubit[3]) # number=45\\n prog.cx(input_qubit[3],input_qubit[0]) # number=46\\n prog.cx(input_qubit[3],input_qubit[0]) # number=34\\n prog.rx(0.11938052083641225,input_qubit[1]) # number=36\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.rx(1.4765485471872026,input_qubit[2]) # number=35\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=41\\n prog.x(input_qubit[0]) # number=42\\n prog.cx(input_qubit[1],input_qubit[0]) # number=43\\n prog.x(input_qubit[4]) # number=30\\n prog.x(input_qubit[1]) # number=10\\n prog.x(input_qubit[2]) # number=11\\n prog.rx(0.45238934211692994,input_qubit[3]) # number=38\\n prog.y(input_qubit[1]) # number=39\\n prog.rx(-2.5258404934861938,input_qubit[1]) # number=25\\n prog.h(input_qubit[3]) # number=29\\n prog.cx(input_qubit[0],input_qubit[3]) # number=22\\n prog.x(input_qubit[3]) # number=23\\n prog.cx(input_qubit[0],input_qubit[3]) # number=24\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.x(input_qubit[0]) # number=13\\n prog.rx(-0.0722566310325653,input_qubit[4]) # number=37\\n prog.x(input_qubit[1]) # number=14\\n prog.cx(input_qubit[0],input_qubit[2]) # number=26\\n prog.x(input_qubit[2]) # number=27\\n prog.h(input_qubit[4]) # number=40\\n prog.cx(input_qubit[0],input_qubit[2]) # number=28\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC1476.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=50\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[0]) # number=38\\n prog.cz(input_qubit[1],input_qubit[0]) # number=39\\n prog.h(input_qubit[0]) # number=40\\n prog.z(input_qubit[1]) # number=30\\n prog.h(input_qubit[0]) # number=32\\n prog.cz(input_qubit[1],input_qubit[0]) # number=33\\n prog.h(input_qubit[0]) # number=34\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=47\\n prog.cz(input_qubit[3],input_qubit[0]) # number=48\\n prog.h(input_qubit[0]) # number=49\\n prog.z(input_qubit[3]) # number=42\\n prog.cx(input_qubit[3],input_qubit[0]) # number=43\\n prog.cx(input_qubit[1],input_qubit[3]) # number=44\\n prog.cx(input_qubit[3],input_qubit[2]) # number=45\\n\\n\\n prog.x(input_qubit[0]) # number=9\\n prog.x(input_qubit[1]) # number=10\\n prog.x(input_qubit[2]) # number=11\\n prog.cx(input_qubit[0],input_qubit[3]) # number=35\\n prog.x(input_qubit[3]) # number=36\\n prog.cx(input_qubit[0],input_qubit[3]) # number=37\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=24\\n prog.x(input_qubit[0]) # number=25\\n prog.cx(input_qubit[1],input_qubit[0]) # number=26\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[3]) # number=16\\n prog.x(input_qubit[3]) # number=46\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n prog.x(input_qubit[1]) # number=22\\n prog.x(input_qubit[1]) # number=23\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n backend = FakeVigo()\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy1149.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=2\\n# total number=21\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import IBMQ\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename='circuit/deutsch-oracle.png')\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n\\n input_qubit = QuantumRegister(n, \\\"qc\\\")\\n target = QuantumRegister(1, \\\"qt\\\")\\n prog = QuantumCircuit(input_qubit, target)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(target)\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n\\n prog.h(input_qubit[1]) # number=1\\n prog.h(target)\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [target])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n #for i in range(n):\\n # prog.measure(input_qubit[i], classicals[i])\\n\\n prog.y(input_qubit[1]) # number=2\\n prog.y(input_qubit[1]) # number=4\\n prog.y(input_qubit[1]) # number=3\\n prog.h(input_qubit[0]) # number=13\\n prog.cz(input_qubit[1],input_qubit[0]) # number=14\\n prog.h(input_qubit[0]) # number=15\\n prog.x(input_qubit[0]) # number=8\\n prog.h(input_qubit[0]) # number=18\\n prog.cz(input_qubit[1],input_qubit[0]) # number=19\\n prog.h(input_qubit[0]) # number=20\\n prog.cx(input_qubit[1],input_qubit[0]) # number=10\\n prog.x(input_qubit[0]) # number=11\\n prog.cx(input_qubit[1],input_qubit[0]) # number=12\\n prog.x(input_qubit[0]) # number=16\\n prog.x(input_qubit[0]) # number=17\\n # circuit end\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n n = 2\\n f = lambda rep: rep[-1]\\n # f = lambda rep: \\\"1\\\" if rep[0:2] == \\\"01\\\" or rep[0:2] == \\\"10\\\" else \\\"0\\\"\\n # f = lambda rep: \\\"0\\\"\\n prog = make_circuit(n, f)\\n sample_shot =2800\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = provider.get_backend(\\\"ibmq_belem\\\")\\n\\n circuit1 = transpile(prog,FakeVigo())\\n circuit1.x(qubit=3)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n prog = circuit1\\n\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n\\n writefile = open(\\\"../data/startQiskit_QC349.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=10\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nimport networkx as nx\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef make_circuit(n:int) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n prog = QuantumCircuit(input_qubit)\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=5\\n\\n for edge in E:\\n k = edge[0]\\n l = edge[1]\\n prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1])\\n prog.p(gamma, k)\\n prog.p(gamma, l)\\n\\n prog.rx(2 * beta, range(len(V)))\\n\\n prog.swap(input_qubit[1],input_qubit[0]) # number=6\\n prog.y(input_qubit[2]) # number=8\\n prog.y(input_qubit[2]) # number=9\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n n = 4\\n V = np.arange(0, n, 1)\\n E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]\\n\\n G = nx.Graph()\\n G.add_nodes_from(V)\\n G.add_weighted_edges_from(E)\\n\\n step_size = 0.1\\n\\n a_gamma = np.arange(0, np.pi, step_size)\\n a_beta = np.arange(0, np.pi, step_size)\\n a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)\\n\\n F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (\\n 1 + np.cos(4 * a_gamma) ** 2)\\n\\n result = np.where(F1 == np.amax(F1))\\n a = list(zip(result[0], result[1]))[0]\\n\\n gamma = a[0] * step_size\\n beta = a[1] * step_size\\n\\n prog = make_circuit(4)\\n sample_shot =3962\\n writefile = open(\\\"../data/startQiskit_QC330.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = provider.get_backend(\\\"ibmq_5_yorktown\\\")\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.measure_all()\\n prog = circuit1\\n\\n info = execute(prog,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=60\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[0]) # number=38\\n prog.cz(input_qubit[1],input_qubit[0]) # number=39\\n prog.h(input_qubit[0]) # number=40\\n prog.h(input_qubit[0]) # number=49\\n prog.cz(input_qubit[1],input_qubit[0]) # number=50\\n prog.h(input_qubit[0]) # number=51\\n prog.cx(input_qubit[1],input_qubit[0]) # number=52\\n prog.z(input_qubit[1]) # number=53\\n prog.cx(input_qubit[1],input_qubit[0]) # number=54\\n prog.h(input_qubit[0]) # number=57\\n prog.cz(input_qubit[1],input_qubit[0]) # number=58\\n prog.h(input_qubit[0]) # number=59\\n prog.h(input_qubit[0]) # number=32\\n prog.cz(input_qubit[1],input_qubit[0]) # number=33\\n prog.h(input_qubit[0]) # number=34\\n prog.x(input_qubit[4]) # number=48\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.cx(input_qubit[3],input_qubit[0]) # number=41\\n prog.z(input_qubit[3]) # number=42\\n prog.cx(input_qubit[3],input_qubit[0]) # number=43\\n prog.cx(input_qubit[1],input_qubit[3]) # number=44\\n\\n\\n prog.x(input_qubit[0]) # number=9\\n prog.h(input_qubit[1]) # number=56\\n prog.x(input_qubit[1]) # number=10\\n prog.x(input_qubit[2]) # number=11\\n prog.rx(-2.9845130209103035,input_qubit[4]) # number=55\\n prog.cx(input_qubit[0],input_qubit[3]) # number=35\\n prog.x(input_qubit[3]) # number=36\\n prog.cx(input_qubit[0],input_qubit[3]) # number=37\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=24\\n prog.x(input_qubit[0]) # number=25\\n prog.cx(input_qubit[1],input_qubit[0]) # number=26\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n prog.x(input_qubit[1]) # number=22\\n prog.x(input_qubit[1]) # number=23\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n backend = BasicAer.get_backend('qasm_simulator')\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit1617.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=33\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=20\\n prog.cz(input_qubit[0],input_qubit[3]) # number=21\\n prog.h(input_qubit[3]) # number=22\\n prog.x(input_qubit[3]) # number=13\\n prog.h(input_qubit[3]) # number=23\\n prog.cz(input_qubit[0],input_qubit[3]) # number=24\\n prog.h(input_qubit[3]) # number=25\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.h(input_qubit[0]) # number=5\\n prog.y(input_qubit[2]) # number=18\\n prog.z(input_qubit[3]) # number=28\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.cx(input_qubit[2],input_qubit[0]) # number=10\\n prog.h(input_qubit[1]) # number=19\\n prog.h(input_qubit[0]) # number=15\\n prog.cz(input_qubit[2],input_qubit[0]) # number=16\\n prog.h(input_qubit[0]) # number=17\\n prog.y(input_qubit[1]) # number=26\\n prog.y(input_qubit[1]) # number=27\\n prog.swap(input_qubit[1],input_qubit[0]) # number=29\\n prog.swap(input_qubit[1],input_qubit[0]) # number=30\\n prog.cx(input_qubit[2],input_qubit[0]) # number=31\\n prog.cx(input_qubit[2],input_qubit[0]) # number=32\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = BasicAer.get_backend('statevector_simulator')\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend).result().get_statevector()\\n qubits = round(log2(len(info)))\\n info = {\\n np.binary_repr(i, qubits): round((info[i]*(info[i].conjugate())).real,3)\\n for i in range(2 ** qubits)\\n }\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_Class2099.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n# @Time : 5/15/20 4:49 PM\\n# @File : grover.py\\n\\n# qubit number=4\\n# total number=19\\nimport cirq\\nimport cirq.google as cg\\nfrom typing import Optional\\nimport sys\\nfrom math import log2\\nimport numpy as np\\n\\n#thatsNoCode\\n\\ndef make_circuit(n: int, input_qubit):\\n c = cirq.Circuit() # circuit begin\\n\\n c.append(cirq.H.on(input_qubit[0])) # number=1\\n c.append(cirq.H.on(input_qubit[1])) # number=2\\n c.append(cirq.H.on(input_qubit[1])) # number=7\\n c.append(cirq.H.on(input_qubit[2])) # number=3\\n c.append(cirq.H.on(input_qubit[3])) # number=4\\n c.append(cirq.H.on(input_qubit[0])) # number=16\\n c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=17\\n c.append(cirq.H.on(input_qubit[0])) # number=18\\n c.append(cirq.H.on(input_qubit[0])) # number=10\\n c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=11\\n c.append(cirq.H.on(input_qubit[0])) # number=12\\n c.append(cirq.CNOT.on(input_qubit[2],input_qubit[0])) # number=8\\n c.append(cirq.H.on(input_qubit[0])) # number=13\\n c.append(cirq.CZ.on(input_qubit[2],input_qubit[0])) # number=14\\n c.append(cirq.H.on(input_qubit[0])) # number=15\\n # circuit end\\n\\n\\n return c\\n\\ndef bitstring(bits):\\n return ''.join(str(int(b)) for b in bits)\\n\\nif __name__ == '__main__':\\n qubit_count = 4\\n\\n input_qubits = [cirq.GridQubit(i, 0) for i in range(qubit_count)]\\n circuit = make_circuit(qubit_count,input_qubits)\\n circuit = cg.optimized_for_sycamore(circuit, optimizer_type='sqrt_iswap')\\n\\n circuit_sample_count =0\\n\\n info = cirq.final_state_vector(circuit)\\n\\n qubits = round(log2(len(info)))\\n frequencies = {\\n np.binary_repr(i, qubits): round((info[i]*(info[i].conjugate())).real,3)\\n for i in range(2 ** qubits)\\n }\\n writefile = open(\\\"../data/startCirq_Class319.csv\\\",\\\"w+\\\")\\n\\n print(format(frequencies),file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n\\n print(circuit.__len__(), file=writefile)\\n print(circuit,file=writefile)\\n\\n\\n writefile.close()\",\n \"# qubit number=4\\n# total number=40\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=24\\n prog.cz(input_qubit[0],input_qubit[3]) # number=25\\n prog.h(input_qubit[3]) # number=26\\n prog.h(input_qubit[3]) # number=21\\n prog.cz(input_qubit[0],input_qubit[3]) # number=22\\n prog.h(input_qubit[3]) # number=23\\n prog.h(input_qubit[3]) # number=27\\n prog.cz(input_qubit[0],input_qubit[3]) # number=28\\n prog.h(input_qubit[3]) # number=29\\n prog.cx(input_qubit[0],input_qubit[3]) # number=30\\n prog.cx(input_qubit[0],input_qubit[3]) # number=37\\n prog.x(input_qubit[3]) # number=38\\n prog.cx(input_qubit[0],input_qubit[3]) # number=39\\n prog.h(input_qubit[3]) # number=33\\n prog.cz(input_qubit[0],input_qubit[3]) # number=34\\n prog.h(input_qubit[3]) # number=35\\n prog.cx(input_qubit[0],input_qubit[3]) # number=18\\n prog.rx(-0.364424747816416,input_qubit[3]) # number=36\\n prog.y(input_qubit[3]) # number=20\\n prog.cx(input_qubit[0],input_qubit[3]) # number=15\\n prog.cx(input_qubit[0],input_qubit[3]) # number=12\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=19\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = FakeVigo()\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy2217.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=12\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nimport networkx as nx\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef make_circuit(n:int) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n prog = QuantumCircuit(input_qubit)\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=5\\n\\n for edge in E:\\n k = edge[0]\\n l = edge[1]\\n prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1])\\n prog.p(gamma, k)\\n prog.p(gamma, l)\\n\\n prog.rx(2 * beta, range(len(V)))\\n\\n prog.swap(input_qubit[1],input_qubit[0]) # number=6\\n prog.swap(input_qubit[1],input_qubit[0]) # number=7\\n prog.cx(input_qubit[1],input_qubit[0]) # number=8\\n prog.cx(input_qubit[1],input_qubit[0]) # number=9\\n prog.y(input_qubit[3]) # number=10\\n prog.y(input_qubit[3]) # number=11\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n n = 4\\n V = np.arange(0, n, 1)\\n E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]\\n\\n G = nx.Graph()\\n G.add_nodes_from(V)\\n G.add_weighted_edges_from(E)\\n\\n step_size = 0.1\\n\\n a_gamma = np.arange(0, np.pi, step_size)\\n a_beta = np.arange(0, np.pi, step_size)\\n a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)\\n\\n F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (\\n 1 + np.cos(4 * a_gamma) ** 2)\\n\\n result = np.where(F1 == np.amax(F1))\\n a = list(zip(result[0], result[1]))[0]\\n\\n gamma = a[0] * step_size\\n beta = a[1] * step_size\\n\\n prog = make_circuit(4)\\n sample_shot =5600\\n writefile = open(\\\"../data/startQiskit_Class197.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = BasicAer.get_backend('statevector_simulator')\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n prog = circuit1\\n\\n info = execute(prog,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=66\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[0]) # number=57\\n prog.cz(input_qubit[4],input_qubit[0]) # number=58\\n prog.h(input_qubit[0]) # number=59\\n prog.z(input_qubit[4]) # number=55\\n prog.cx(input_qubit[4],input_qubit[0]) # number=56\\n prog.h(input_qubit[2]) # number=50\\n prog.cz(input_qubit[4],input_qubit[2]) # number=51\\n prog.h(input_qubit[2]) # number=52\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n\\n\\n prog.h(input_qubit[0]) # number=28\\n prog.h(input_qubit[0]) # number=63\\n prog.cz(input_qubit[3],input_qubit[0]) # number=64\\n prog.h(input_qubit[0]) # number=65\\n prog.z(input_qubit[3]) # number=61\\n prog.cx(input_qubit[3],input_qubit[0]) # number=62\\n prog.cz(input_qubit[1],input_qubit[0]) # number=29\\n prog.h(input_qubit[0]) # number=30\\n prog.h(input_qubit[0]) # number=43\\n prog.cz(input_qubit[1],input_qubit[0]) # number=44\\n prog.h(input_qubit[0]) # number=45\\n prog.cx(input_qubit[1],input_qubit[0]) # number=35\\n prog.cx(input_qubit[1],input_qubit[0]) # number=38\\n prog.x(input_qubit[0]) # number=39\\n prog.cx(input_qubit[1],input_qubit[0]) # number=40\\n prog.cx(input_qubit[1],input_qubit[0]) # number=37\\n prog.h(input_qubit[0]) # number=46\\n prog.cz(input_qubit[1],input_qubit[0]) # number=47\\n prog.h(input_qubit[0]) # number=48\\n prog.cx(input_qubit[1],input_qubit[0]) # number=27\\n prog.x(input_qubit[1]) # number=10\\n prog.x(input_qubit[2]) # number=11\\n prog.x(input_qubit[3]) # number=12\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.x(input_qubit[0]) # number=13\\n prog.cx(input_qubit[0],input_qubit[1]) # number=22\\n prog.y(input_qubit[2]) # number=41\\n prog.x(input_qubit[1]) # number=23\\n prog.cx(input_qubit[0],input_qubit[1]) # number=24\\n prog.rx(1.0398671683382215,input_qubit[2]) # number=31\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n backend = BasicAer.get_backend('qasm_simulator')\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit1762.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=49\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=46\\n prog.cz(input_qubit[0],input_qubit[3]) # number=47\\n prog.h(input_qubit[3]) # number=48\\n prog.h(input_qubit[3]) # number=40\\n prog.cz(input_qubit[0],input_qubit[3]) # number=41\\n prog.h(input_qubit[3]) # number=42\\n prog.h(input_qubit[3]) # number=43\\n prog.cz(input_qubit[0],input_qubit[3]) # number=44\\n prog.h(input_qubit[3]) # number=45\\n prog.x(input_qubit[3]) # number=34\\n prog.cx(input_qubit[0],input_qubit[3]) # number=35\\n prog.cx(input_qubit[0],input_qubit[3]) # number=25\\n prog.cx(input_qubit[0],input_qubit[3]) # number=12\\n prog.h(input_qubit[2]) # number=30\\n prog.cz(input_qubit[0],input_qubit[2]) # number=31\\n prog.h(input_qubit[2]) # number=32\\n prog.x(input_qubit[2]) # number=21\\n prog.h(input_qubit[2]) # number=36\\n prog.cz(input_qubit[0],input_qubit[2]) # number=37\\n prog.h(input_qubit[2]) # number=38\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.h(input_qubit[0]) # number=5\\n prog.h(input_qubit[3]) # number=16\\n prog.cz(input_qubit[1],input_qubit[3]) # number=17\\n prog.h(input_qubit[3]) # number=18\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n prog.h(input_qubit[2]) # number=39\\n\\n prog.h(input_qubit[0]) # number=26\\n prog.cz(input_qubit[3],input_qubit[0]) # number=27\\n prog.h(input_qubit[0]) # number=28\\n prog.cx(input_qubit[3],input_qubit[0]) # number=14\\n prog.y(input_qubit[2]) # number=29\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC3235.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=10\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nimport networkx as nx\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef make_circuit(n:int) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n prog = QuantumCircuit(input_qubit)\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.cz(input_qubit[3],input_qubit[2]) # number=8\\n prog.h(input_qubit[2]) # number=9\\n prog.z(input_qubit[3]) # number=5\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n\\n for edge in E:\\n k = edge[0]\\n l = edge[1]\\n prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1])\\n prog.p(gamma, k)\\n prog.p(gamma, l)\\n\\n prog.rx(2 * beta, range(len(V)))\\n\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n n = 4\\n V = np.arange(0, n, 1)\\n E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]\\n\\n G = nx.Graph()\\n G.add_nodes_from(V)\\n G.add_weighted_edges_from(E)\\n\\n step_size = 0.1\\n\\n a_gamma = np.arange(0, np.pi, step_size)\\n a_beta = np.arange(0, np.pi, step_size)\\n a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)\\n\\n F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (\\n 1 + np.cos(4 * a_gamma) ** 2)\\n\\n result = np.where(F1 == np.amax(F1))\\n a = list(zip(result[0], result[1]))[0]\\n\\n gamma = a[0] * step_size\\n beta = a[1] * step_size\\n\\n prog = make_circuit(4)\\n sample_shot =5200\\n writefile = open(\\\"../data/startQiskit_noisy94.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = FakeYorktown()\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.measure_all()\\n prog = circuit1\\n\\n info = execute(prog,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=34\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=31\\n prog.cz(input_qubit[0],input_qubit[3]) # number=32\\n prog.h(input_qubit[3]) # number=33\\n prog.h(input_qubit[3]) # number=30\\n prog.x(input_qubit[3]) # number=11\\n prog.h(input_qubit[3]) # number=13\\n prog.cz(input_qubit[0],input_qubit[3]) # number=14\\n prog.h(input_qubit[1]) # number=18\\n prog.cz(input_qubit[3],input_qubit[1]) # number=19\\n prog.z(input_qubit[3]) # number=25\\n prog.h(input_qubit[1]) # number=20\\n prog.rx(-3.141592653589793,input_qubit[3]) # number=26\\n prog.h(input_qubit[3]) # number=15\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[2]) # number=17\\n prog.h(input_qubit[3]) # number=4\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.h(input_qubit[0]) # number=27\\n prog.cz(input_qubit[1],input_qubit[0]) # number=28\\n prog.h(input_qubit[0]) # number=29\\n prog.cx(input_qubit[1],input_qubit[0]) # number=22\\n prog.x(input_qubit[1]) # number=23\\n prog.x(input_qubit[1]) # number=24\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = BasicAer.get_backend('qasm_simulator')\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit2205.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=2\\n# total number=10\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import IBMQ\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename='circuit/deutsch-oracle.png')\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n\\n input_qubit = QuantumRegister(n, \\\"qc\\\")\\n target = QuantumRegister(1, \\\"qt\\\")\\n prog = QuantumCircuit(input_qubit, target)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(target)\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n\\n prog.h(input_qubit[1]) # number=1\\n prog.h(target)\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [target])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n #for i in range(n):\\n # prog.measure(input_qubit[i], classicals[i])\\n\\n prog.x(input_qubit[1]) # number=2\\n prog.h(input_qubit[1]) # number=7\\n prog.cz(input_qubit[0],input_qubit[1]) # number=8\\n prog.h(input_qubit[1]) # number=9\\n prog.x(input_qubit[1]) # number=5\\n prog.cx(input_qubit[0],input_qubit[1]) # number=6\\n # circuit end\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n n = 2\\n f = lambda rep: rep[-1]\\n # f = lambda rep: \\\"1\\\" if rep[0:2] == \\\"01\\\" or rep[0:2] == \\\"10\\\" else \\\"0\\\"\\n # f = lambda rep: \\\"0\\\"\\n prog = make_circuit(n, f)\\n sample_shot =2800\\n backend = FakeVigo()\\n\\n circuit1 = transpile(prog,FakeVigo())\\n circuit1.x(qubit=3)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n prog = circuit1\\n\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n\\n writefile = open(\\\"../data/startQiskit_noisy61.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=77\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\n\\ndef build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename=(kernel + '-oracle.png'))\\n return oracle\\n\\n\\ndef build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the Bernstein-Vazirani circuit\\n zero = np.binary_repr(0, n)\\n b = f(zero)\\n\\n # initial n + 1 bits\\n input_qubit = QuantumRegister(n+1, \\\"qc\\\")\\n classicals = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classicals)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(input_qubit[n])\\n # circuit begin\\n prog.h(input_qubit[1]) # number=1\\n prog.h(input_qubit[1]) # number=70\\n prog.rx(-0.09738937226128368,input_qubit[2]) # number=2\\n prog.h(input_qubit[1]) # number=33\\n prog.y(input_qubit[2]) # number=56\\n prog.cz(input_qubit[2],input_qubit[1]) # number=34\\n prog.h(input_qubit[1]) # number=35\\n prog.h(input_qubit[1]) # number=3\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.h(input_qubit[n])\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [input_qubit[n]])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n return prog\\n\\n\\ndef get_statevector(prog: QuantumCircuit) -> Any:\\n state_backend = Aer.get_backend('statevector_simulator')\\n statevec = execute(prog, state_backend).result()\\n quantum_state = statevec.get_statevector()\\n qubits = round(log2(len(quantum_state)))\\n quantum_state = {\\n \\\"|\\\" + np.binary_repr(i, qubits) + \\\">\\\": quantum_state[i]\\n for i in range(2 ** qubits)\\n }\\n return quantum_state\\n\\n\\ndef evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any:\\n # Q: which backend should we use?\\n\\n # get state vector\\n quantum_state = get_statevector(prog)\\n\\n # get simulate results\\n\\n # provider = IBMQ.load_account()\\n # backend = provider.get_backend(backend_str)\\n # qobj = compile(prog, backend, shots)\\n # job = backend.run(qobj)\\n # job.result()\\n backend = Aer.get_backend(backend_str)\\n # transpile/schedule -> assemble -> backend.run\\n results = execute(prog, backend, shots=shots).result()\\n counts = results.get_counts()\\n a = Counter(counts).most_common(1)[0][0][::-1]\\n\\n return {\\n \\\"measurements\\\": counts,\\n # \\\"state\\\": statevec,\\n \\\"quantum_state\\\": quantum_state,\\n \\\"a\\\": a,\\n \\\"b\\\": b\\n }\\n\\n\\ndef bernstein_test_1(rep: str):\\n \\\"\\\"\\\"011 . x + 1\\\"\\\"\\\"\\n a = \\\"011\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_2(rep: str):\\n \\\"\\\"\\\"000 . x + 0\\\"\\\"\\\"\\n a = \\\"000\\\"\\n b = \\\"0\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_3(rep: str):\\n \\\"\\\"\\\"111 . x + 1\\\"\\\"\\\"\\n a = \\\"111\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n n = 2\\n a = \\\"11\\\"\\n b = \\\"1\\\"\\n f = lambda rep: \\\\\\n bitwise_xor(bitwise_dot(a, rep), b)\\n prog = build_circuit(n, f)\\n sample_shot =4000\\n writefile = open(\\\"../data/startQiskit_QC429.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = provider.get_backend(\\\"ibmq_belem\\\")\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.h(qubit=2)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n\\n info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=51\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\n\\ndef build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename=(kernel + '-oracle.png'))\\n return oracle\\n\\n\\ndef build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the Bernstein-Vazirani circuit\\n zero = np.binary_repr(0, n)\\n b = f(zero)\\n\\n # initial n + 1 bits\\n input_qubit = QuantumRegister(n+1, \\\"qc\\\")\\n classicals = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classicals)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(input_qubit[n])\\n # circuit begin\\n prog.h(input_qubit[1]) # number=1\\n prog.h(input_qubit[2]) # number=38\\n prog.cz(input_qubit[0],input_qubit[2]) # number=39\\n prog.h(input_qubit[2]) # number=40\\n prog.h(input_qubit[2]) # number=48\\n prog.cz(input_qubit[0],input_qubit[2]) # number=49\\n prog.h(input_qubit[2]) # number=50\\n prog.h(input_qubit[2]) # number=42\\n prog.cz(input_qubit[0],input_qubit[2]) # number=43\\n prog.h(input_qubit[2]) # number=44\\n prog.cx(input_qubit[0],input_qubit[2]) # number=45\\n prog.x(input_qubit[2]) # number=46\\n prog.cx(input_qubit[0],input_qubit[2]) # number=47\\n prog.cx(input_qubit[0],input_qubit[2]) # number=37\\n prog.cx(input_qubit[0],input_qubit[2]) # number=33\\n prog.h(input_qubit[2]) # number=25\\n prog.cz(input_qubit[0],input_qubit[2]) # number=26\\n prog.h(input_qubit[2]) # number=27\\n prog.h(input_qubit[1]) # number=7\\n prog.cz(input_qubit[2],input_qubit[1]) # number=8\\n prog.rx(0.17592918860102857,input_qubit[2]) # number=34\\n prog.rx(-0.3989822670059037,input_qubit[1]) # number=30\\n prog.h(input_qubit[1]) # number=9\\n prog.h(input_qubit[1]) # number=18\\n prog.cz(input_qubit[2],input_qubit[1]) # number=19\\n prog.h(input_qubit[1]) # number=20\\n prog.y(input_qubit[1]) # number=14\\n prog.h(input_qubit[1]) # number=22\\n prog.cz(input_qubit[2],input_qubit[1]) # number=23\\n prog.h(input_qubit[1]) # number=24\\n prog.z(input_qubit[2]) # number=3\\n prog.z(input_qubit[1]) # number=41\\n prog.x(input_qubit[1]) # number=17\\n prog.y(input_qubit[2]) # number=5\\n prog.x(input_qubit[2]) # number=21\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.h(input_qubit[n])\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [input_qubit[n]])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n return prog\\n\\n\\ndef get_statevector(prog: QuantumCircuit) -> Any:\\n state_backend = Aer.get_backend('statevector_simulator')\\n statevec = execute(prog, state_backend).result()\\n quantum_state = statevec.get_statevector()\\n qubits = round(log2(len(quantum_state)))\\n quantum_state = {\\n \\\"|\\\" + np.binary_repr(i, qubits) + \\\">\\\": quantum_state[i]\\n for i in range(2 ** qubits)\\n }\\n return quantum_state\\n\\n\\ndef evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any:\\n # Q: which backend should we use?\\n\\n # get state vector\\n quantum_state = get_statevector(prog)\\n\\n # get simulate results\\n\\n # provider = IBMQ.load_account()\\n # backend = provider.get_backend(backend_str)\\n # qobj = compile(prog, backend, shots)\\n # job = backend.run(qobj)\\n # job.result()\\n backend = Aer.get_backend(backend_str)\\n # transpile/schedule -> assemble -> backend.run\\n results = execute(prog, backend, shots=shots).result()\\n counts = results.get_counts()\\n a = Counter(counts).most_common(1)[0][0][::-1]\\n\\n return {\\n \\\"measurements\\\": counts,\\n # \\\"state\\\": statevec,\\n \\\"quantum_state\\\": quantum_state,\\n \\\"a\\\": a,\\n \\\"b\\\": b\\n }\\n\\n\\ndef bernstein_test_1(rep: str):\\n \\\"\\\"\\\"011 . x + 1\\\"\\\"\\\"\\n a = \\\"011\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_2(rep: str):\\n \\\"\\\"\\\"000 . x + 0\\\"\\\"\\\"\\n a = \\\"000\\\"\\n b = \\\"0\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_3(rep: str):\\n \\\"\\\"\\\"111 . x + 1\\\"\\\"\\\"\\n a = \\\"111\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n n = 2\\n a = \\\"11\\\"\\n b = \\\"1\\\"\\n f = lambda rep: \\\\\\n bitwise_xor(bitwise_dot(a, rep), b)\\n prog = build_circuit(n, f)\\n sample_shot =4000\\n writefile = open(\\\"../data/startQiskit_Class254.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = BasicAer.get_backend('statevector_simulator')\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.h(qubit=2)\\n circuit1.x(qubit=3)\\n\\n info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=2\\n# total number=12\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import IBMQ\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename='circuit/deutsch-oracle.png')\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n\\n input_qubit = QuantumRegister(n, \\\"qc\\\")\\n target = QuantumRegister(1, \\\"qt\\\")\\n prog = QuantumCircuit(input_qubit, target)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(target)\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n\\n prog.h(input_qubit[1]) # number=1\\n prog.cx(input_qubit[0],input_qubit[1]) # number=6\\n prog.cx(input_qubit[0],input_qubit[1]) # number=9\\n prog.x(input_qubit[1]) # number=10\\n prog.cx(input_qubit[0],input_qubit[1]) # number=11\\n prog.cx(input_qubit[0],input_qubit[1]) # number=8\\n prog.cx(input_qubit[0],input_qubit[1]) # number=4\\n prog.h(target)\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [target])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n prog.x(input_qubit[1]) # number=2\\n prog.x(input_qubit[1]) # number=3\\n # circuit end\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n n = 2\\n f = lambda rep: rep[-1]\\n # f = lambda rep: \\\"1\\\" if rep[0:2] == \\\"01\\\" or rep[0:2] == \\\"10\\\" else \\\"0\\\"\\n # f = lambda rep: \\\"0\\\"\\n prog = make_circuit(n, f)\\n sample_shot =2800\\n backend = BasicAer.get_backend('statevector_simulator')\\n\\n circuit1 = transpile(prog,FakeVigo())\\n circuit1.x(qubit=3)\\n circuit1.x(qubit=3)\\n prog = circuit1\\n\\n\\n info = execute(prog, backend=backend).result().get_statevector()\\n qubits = round(log2(len(info)))\\n info = {\\n np.binary_repr(i, qubits): round((info[i]*(info[i].conjugate())).real,3)\\n for i in range(2 ** qubits)\\n }\\n\\n writefile = open(\\\"../data/startQiskit_Class161.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=37\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=31\\n prog.cz(input_qubit[0],input_qubit[3]) # number=32\\n prog.h(input_qubit[3]) # number=33\\n prog.x(input_qubit[3]) # number=27\\n prog.cx(input_qubit[0],input_qubit[3]) # number=28\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.cx(input_qubit[3],input_qubit[0]) # number=34\\n prog.z(input_qubit[3]) # number=35\\n prog.cx(input_qubit[3],input_qubit[0]) # number=36\\n prog.h(input_qubit[3]) # number=4\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.cx(input_qubit[2],input_qubit[0]) # number=10\\n prog.h(input_qubit[0]) # number=14\\n prog.h(input_qubit[1]) # number=30\\n prog.cz(input_qubit[2],input_qubit[0]) # number=15\\n prog.h(input_qubit[0]) # number=16\\n prog.cx(input_qubit[0],input_qubit[2]) # number=20\\n prog.x(input_qubit[2]) # number=21\\n prog.cx(input_qubit[0],input_qubit[2]) # number=22\\n prog.cx(input_qubit[0],input_qubit[2]) # number=17\\n prog.cx(input_qubit[0],input_qubit[2]) # number=23\\n prog.x(input_qubit[2]) # number=24\\n prog.cx(input_qubit[0],input_qubit[2]) # number=25\\n prog.cx(input_qubit[0],input_qubit[2]) # number=19\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = FakeVigo()\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy2117.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=37\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.cx(input_qubit[0],input_qubit[3]) # number=13\\n prog.cx(input_qubit[0],input_qubit[3]) # number=17\\n prog.x(input_qubit[3]) # number=18\\n prog.cx(input_qubit[0],input_qubit[3]) # number=19\\n prog.cx(input_qubit[0],input_qubit[3]) # number=15\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=12\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.cx(input_qubit[0],input_qubit[3]) # number=27\\n prog.x(input_qubit[3]) # number=28\\n prog.h(input_qubit[3]) # number=30\\n prog.cz(input_qubit[0],input_qubit[3]) # number=31\\n prog.h(input_qubit[3]) # number=32\\n prog.h(input_qubit[0]) # number=33\\n prog.cz(input_qubit[3],input_qubit[0]) # number=34\\n prog.rx(0.33300882128051834,input_qubit[2]) # number=36\\n prog.h(input_qubit[0]) # number=35\\n prog.cx(input_qubit[3],input_qubit[0]) # number=23\\n prog.z(input_qubit[3]) # number=24\\n prog.cx(input_qubit[3],input_qubit[0]) # number=25\\n prog.cx(input_qubit[3],input_qubit[0]) # number=22\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[2]) # number=10\\n prog.y(input_qubit[2]) # number=11\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC2298.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=13\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nimport networkx as nx\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef make_circuit(n:int) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n prog = QuantumCircuit(input_qubit)\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=5\\n\\n for edge in E:\\n k = edge[0]\\n l = edge[1]\\n prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1])\\n prog.p(gamma, k)\\n prog.p(gamma, l)\\n\\n prog.rx(2 * beta, range(len(V)))\\n\\n prog.swap(input_qubit[1],input_qubit[0]) # number=6\\n prog.swap(input_qubit[1],input_qubit[0]) # number=7\\n prog.cx(input_qubit[1],input_qubit[0]) # number=8\\n prog.cx(input_qubit[1],input_qubit[0]) # number=9\\n prog.h(input_qubit[3]) # number=10\\n prog.cx(input_qubit[1],input_qubit[0]) # number=11\\n prog.cx(input_qubit[1],input_qubit[0]) # number=12\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n n = 4\\n V = np.arange(0, n, 1)\\n E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]\\n\\n G = nx.Graph()\\n G.add_nodes_from(V)\\n G.add_weighted_edges_from(E)\\n\\n step_size = 0.1\\n\\n a_gamma = np.arange(0, np.pi, step_size)\\n a_beta = np.arange(0, np.pi, step_size)\\n a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)\\n\\n F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (\\n 1 + np.cos(4 * a_gamma) ** 2)\\n\\n result = np.where(F1 == np.amax(F1))\\n a = list(zip(result[0], result[1]))[0]\\n\\n gamma = a[0] * step_size\\n beta = a[1] * step_size\\n\\n prog = make_circuit(4)\\n sample_shot =5600\\n writefile = open(\\\"../data/startQiskit_QC402.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = provider.get_backend(\\\"ibmq_5_yorktown\\\")\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.measure_all()\\n prog = circuit1\\n\\n info = execute(prog,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=50\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[0]) # number=38\\n prog.cz(input_qubit[1],input_qubit[0]) # number=39\\n prog.h(input_qubit[0]) # number=40\\n prog.cx(input_qubit[1],input_qubit[0]) # number=47\\n prog.z(input_qubit[1]) # number=48\\n prog.cx(input_qubit[1],input_qubit[0]) # number=49\\n prog.h(input_qubit[0]) # number=32\\n prog.cz(input_qubit[1],input_qubit[0]) # number=33\\n prog.h(input_qubit[0]) # number=34\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.cx(input_qubit[3],input_qubit[0]) # number=41\\n prog.z(input_qubit[3]) # number=42\\n prog.cx(input_qubit[3],input_qubit[0]) # number=43\\n prog.cx(input_qubit[1],input_qubit[3]) # number=44\\n prog.cx(input_qubit[3],input_qubit[2]) # number=45\\n\\n\\n prog.x(input_qubit[0]) # number=9\\n prog.x(input_qubit[1]) # number=10\\n prog.x(input_qubit[2]) # number=11\\n prog.cx(input_qubit[0],input_qubit[3]) # number=35\\n prog.x(input_qubit[3]) # number=36\\n prog.cx(input_qubit[0],input_qubit[3]) # number=37\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=24\\n prog.x(input_qubit[0]) # number=25\\n prog.cx(input_qubit[1],input_qubit[0]) # number=26\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[3]) # number=16\\n prog.x(input_qubit[3]) # number=46\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n prog.x(input_qubit[1]) # number=22\\n prog.x(input_qubit[1]) # number=23\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n backend = FakeVigo()\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy1150.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=49\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[4]) # number=21\\n prog.h(input_qubit[0]) # number=43\\n prog.cz(input_qubit[4],input_qubit[0]) # number=44\\n prog.h(input_qubit[0]) # number=45\\n prog.cx(input_qubit[4],input_qubit[0]) # number=46\\n prog.z(input_qubit[4]) # number=47\\n prog.cx(input_qubit[4],input_qubit[0]) # number=48\\n prog.h(input_qubit[0]) # number=37\\n prog.cz(input_qubit[4],input_qubit[0]) # number=38\\n prog.h(input_qubit[0]) # number=39\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.rx(-1.0430087609918113,input_qubit[4]) # number=36\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=40\\n prog.x(input_qubit[0]) # number=41\\n prog.cx(input_qubit[1],input_qubit[0]) # number=42\\n prog.x(input_qubit[1]) # number=10\\n prog.rx(-0.06597344572538572,input_qubit[3]) # number=27\\n prog.cx(input_qubit[0],input_qubit[2]) # number=22\\n prog.x(input_qubit[2]) # number=23\\n prog.h(input_qubit[2]) # number=28\\n prog.cz(input_qubit[0],input_qubit[2]) # number=29\\n prog.h(input_qubit[2]) # number=30\\n prog.x(input_qubit[3]) # number=12\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.x(input_qubit[0]) # number=13\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[3]) # number=16\\n prog.h(input_qubit[4]) # number=35\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.rx(2.4912829742967055,input_qubit[2]) # number=26\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[2]) # number=25\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC1236.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=37\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=16\\n prog.cz(input_qubit[0],input_qubit[3]) # number=17\\n prog.rx(-0.5686282702997527,input_qubit[3]) # number=32\\n prog.h(input_qubit[3]) # number=18\\n prog.h(input_qubit[3]) # number=26\\n prog.cz(input_qubit[0],input_qubit[3]) # number=27\\n prog.h(input_qubit[3]) # number=28\\n prog.x(input_qubit[3]) # number=21\\n prog.rx(0.4241150082346221,input_qubit[2]) # number=33\\n prog.cx(input_qubit[0],input_qubit[3]) # number=22\\n prog.cx(input_qubit[0],input_qubit[3]) # number=12\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=23\\n prog.cz(input_qubit[1],input_qubit[2]) # number=24\\n prog.h(input_qubit[2]) # number=25\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=34\\n prog.cz(input_qubit[2],input_qubit[0]) # number=35\\n prog.h(input_qubit[0]) # number=36\\n prog.z(input_qubit[2]) # number=30\\n prog.cx(input_qubit[2],input_qubit[0]) # number=31\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[0]) # number=14\\n prog.y(input_qubit[0]) # number=15\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC2165.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=37\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\n\\ndef build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename=(kernel + '-oracle.png'))\\n return oracle\\n\\n\\ndef build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the Bernstein-Vazirani circuit\\n zero = np.binary_repr(0, n)\\n b = f(zero)\\n\\n # initial n + 1 bits\\n input_qubit = QuantumRegister(n+1, \\\"qc\\\")\\n classicals = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classicals)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(input_qubit[n])\\n # circuit begin\\n prog.h(input_qubit[1]) # number=1\\n prog.cx(input_qubit[0],input_qubit[2]) # number=11\\n prog.cx(input_qubit[0],input_qubit[2]) # number=31\\n prog.x(input_qubit[2]) # number=32\\n prog.h(input_qubit[2]) # number=34\\n prog.cz(input_qubit[0],input_qubit[2]) # number=35\\n prog.h(input_qubit[2]) # number=36\\n prog.h(input_qubit[2]) # number=25\\n prog.cz(input_qubit[0],input_qubit[2]) # number=26\\n prog.h(input_qubit[2]) # number=27\\n prog.h(input_qubit[1]) # number=7\\n prog.cz(input_qubit[2],input_qubit[1]) # number=8\\n prog.rx(-0.3989822670059037,input_qubit[1]) # number=30\\n prog.h(input_qubit[1]) # number=9\\n prog.h(input_qubit[1]) # number=18\\n prog.cz(input_qubit[2],input_qubit[1]) # number=19\\n prog.h(input_qubit[1]) # number=20\\n prog.y(input_qubit[1]) # number=14\\n prog.h(input_qubit[1]) # number=22\\n prog.cz(input_qubit[2],input_qubit[1]) # number=23\\n prog.h(input_qubit[1]) # number=24\\n prog.z(input_qubit[2]) # number=3\\n prog.x(input_qubit[1]) # number=17\\n prog.y(input_qubit[2]) # number=5\\n prog.x(input_qubit[2]) # number=21\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.h(input_qubit[n])\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [input_qubit[n]])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n return prog\\n\\n\\ndef get_statevector(prog: QuantumCircuit) -> Any:\\n state_backend = Aer.get_backend('statevector_simulator')\\n statevec = execute(prog, state_backend).result()\\n quantum_state = statevec.get_statevector()\\n qubits = round(log2(len(quantum_state)))\\n quantum_state = {\\n \\\"|\\\" + np.binary_repr(i, qubits) + \\\">\\\": quantum_state[i]\\n for i in range(2 ** qubits)\\n }\\n return quantum_state\\n\\n\\ndef evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any:\\n # Q: which backend should we use?\\n\\n # get state vector\\n quantum_state = get_statevector(prog)\\n\\n # get simulate results\\n\\n # provider = IBMQ.load_account()\\n # backend = provider.get_backend(backend_str)\\n # qobj = compile(prog, backend, shots)\\n # job = backend.run(qobj)\\n # job.result()\\n backend = Aer.get_backend(backend_str)\\n # transpile/schedule -> assemble -> backend.run\\n results = execute(prog, backend, shots=shots).result()\\n counts = results.get_counts()\\n a = Counter(counts).most_common(1)[0][0][::-1]\\n\\n return {\\n \\\"measurements\\\": counts,\\n # \\\"state\\\": statevec,\\n \\\"quantum_state\\\": quantum_state,\\n \\\"a\\\": a,\\n \\\"b\\\": b\\n }\\n\\n\\ndef bernstein_test_1(rep: str):\\n \\\"\\\"\\\"011 . x + 1\\\"\\\"\\\"\\n a = \\\"011\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_2(rep: str):\\n \\\"\\\"\\\"000 . x + 0\\\"\\\"\\\"\\n a = \\\"000\\\"\\n b = \\\"0\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_3(rep: str):\\n \\\"\\\"\\\"111 . x + 1\\\"\\\"\\\"\\n a = \\\"111\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n n = 2\\n a = \\\"11\\\"\\n b = \\\"1\\\"\\n f = lambda rep: \\\\\\n bitwise_xor(bitwise_dot(a, rep), b)\\n prog = build_circuit(n, f)\\n sample_shot =4000\\n writefile = open(\\\"../data/startQiskit_QC195.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = provider.get_backend(\\\"ibmq_5_yorktown\\\")\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.h(qubit=2)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n\\n info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=43\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.cx(input_qubit[0],input_qubit[3]) # number=13\\n prog.cx(input_qubit[0],input_qubit[3]) # number=17\\n prog.x(input_qubit[3]) # number=18\\n prog.cx(input_qubit[0],input_qubit[3]) # number=19\\n prog.cx(input_qubit[0],input_qubit[3]) # number=15\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=12\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=37\\n prog.cz(input_qubit[0],input_qubit[3]) # number=38\\n prog.h(input_qubit[3]) # number=39\\n prog.cx(input_qubit[0],input_qubit[3]) # number=40\\n prog.x(input_qubit[3]) # number=41\\n prog.cx(input_qubit[0],input_qubit[3]) # number=42\\n prog.h(input_qubit[3]) # number=30\\n prog.cz(input_qubit[0],input_qubit[3]) # number=31\\n prog.h(input_qubit[3]) # number=32\\n prog.h(input_qubit[0]) # number=33\\n prog.cz(input_qubit[3],input_qubit[0]) # number=34\\n prog.rx(0.33300882128051834,input_qubit[2]) # number=36\\n prog.h(input_qubit[0]) # number=35\\n prog.cx(input_qubit[3],input_qubit[0]) # number=23\\n prog.z(input_qubit[3]) # number=24\\n prog.cx(input_qubit[3],input_qubit[0]) # number=25\\n prog.cx(input_qubit[3],input_qubit[0]) # number=22\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[2]) # number=10\\n prog.y(input_qubit[2]) # number=11\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = BasicAer.get_backend('qasm_simulator')\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit2824.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=47\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=31\\n prog.cz(input_qubit[0],input_qubit[3]) # number=32\\n prog.h(input_qubit[3]) # number=33\\n prog.cx(input_qubit[0],input_qubit[3]) # number=44\\n prog.x(input_qubit[3]) # number=45\\n prog.cx(input_qubit[0],input_qubit[3]) # number=46\\n prog.h(input_qubit[3]) # number=34\\n prog.cz(input_qubit[0],input_qubit[3]) # number=35\\n prog.h(input_qubit[3]) # number=36\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.cx(input_qubit[3],input_qubit[0]) # number=38\\n prog.z(input_qubit[3]) # number=39\\n prog.cx(input_qubit[3],input_qubit[0]) # number=40\\n prog.h(input_qubit[3]) # number=4\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.h(input_qubit[0]) # number=41\\n prog.cz(input_qubit[2],input_qubit[0]) # number=42\\n prog.h(input_qubit[0]) # number=43\\n prog.y(input_qubit[3]) # number=37\\n prog.h(input_qubit[0]) # number=14\\n prog.h(input_qubit[1]) # number=30\\n prog.cz(input_qubit[2],input_qubit[0]) # number=15\\n prog.h(input_qubit[0]) # number=16\\n prog.cx(input_qubit[0],input_qubit[2]) # number=20\\n prog.x(input_qubit[2]) # number=21\\n prog.cx(input_qubit[0],input_qubit[2]) # number=22\\n prog.cx(input_qubit[0],input_qubit[2]) # number=17\\n prog.cx(input_qubit[0],input_qubit[2]) # number=23\\n prog.x(input_qubit[2]) # number=24\\n prog.cx(input_qubit[0],input_qubit[2]) # number=25\\n prog.cx(input_qubit[0],input_qubit[2]) # number=19\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = BasicAer.get_backend('qasm_simulator')\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit3157.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=35\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.cx(input_qubit[0],input_qubit[3]) # number=13\\n prog.cx(input_qubit[0],input_qubit[3]) # number=17\\n prog.x(input_qubit[3]) # number=18\\n prog.rx(-3.1101767270538954,input_qubit[1]) # number=27\\n prog.cx(input_qubit[0],input_qubit[3]) # number=19\\n prog.cx(input_qubit[0],input_qubit[3]) # number=15\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=12\\n prog.h(input_qubit[1]) # number=26\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.x(input_qubit[3]) # number=29\\n prog.h(input_qubit[2]) # number=7\\n prog.cx(input_qubit[3],input_qubit[0]) # number=20\\n prog.cx(input_qubit[3],input_qubit[0]) # number=23\\n prog.cx(input_qubit[3],input_qubit[0]) # number=32\\n prog.z(input_qubit[3]) # number=33\\n prog.cx(input_qubit[3],input_qubit[0]) # number=34\\n prog.cx(input_qubit[3],input_qubit[0]) # number=25\\n prog.cx(input_qubit[3],input_qubit[0]) # number=22\\n prog.h(input_qubit[3]) # number=8\\n prog.z(input_qubit[3]) # number=28\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[2]) # number=10\\n prog.y(input_qubit[2]) # number=11\\n prog.x(input_qubit[1]) # number=30\\n prog.x(input_qubit[1]) # number=31\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = BasicAer.get_backend('qasm_simulator')\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit2557.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=63\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n\\n\\n prog.h(input_qubit[0]) # number=31\\n prog.y(input_qubit[1]) # number=56\\n prog.cz(input_qubit[1],input_qubit[0]) # number=32\\n prog.h(input_qubit[1]) # number=52\\n prog.h(input_qubit[0]) # number=33\\n prog.h(input_qubit[1]) # number=44\\n prog.cz(input_qubit[0],input_qubit[1]) # number=45\\n prog.h(input_qubit[1]) # number=46\\n prog.h(input_qubit[1]) # number=57\\n prog.cz(input_qubit[0],input_qubit[1]) # number=58\\n prog.h(input_qubit[1]) # number=59\\n prog.cx(input_qubit[0],input_qubit[1]) # number=60\\n prog.x(input_qubit[1]) # number=61\\n prog.cx(input_qubit[0],input_qubit[1]) # number=62\\n prog.cx(input_qubit[0],input_qubit[1]) # number=55\\n prog.h(input_qubit[1]) # number=48\\n prog.cz(input_qubit[0],input_qubit[1]) # number=49\\n prog.h(input_qubit[1]) # number=50\\n prog.x(input_qubit[0]) # number=26\\n prog.cx(input_qubit[1],input_qubit[0]) # number=27\\n prog.h(input_qubit[1]) # number=37\\n prog.cz(input_qubit[0],input_qubit[1]) # number=38\\n prog.h(input_qubit[1]) # number=39\\n prog.x(input_qubit[1]) # number=35\\n prog.cx(input_qubit[0],input_qubit[1]) # number=36\\n prog.x(input_qubit[2]) # number=11\\n prog.x(input_qubit[3]) # number=12\\n prog.cx(input_qubit[3],input_qubit[2]) # number=43\\n prog.cx(input_qubit[3],input_qubit[2]) # number=47\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.x(input_qubit[0]) # number=13\\n prog.cx(input_qubit[0],input_qubit[1]) # number=22\\n prog.x(input_qubit[1]) # number=23\\n prog.cx(input_qubit[0],input_qubit[1]) # number=24\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[1]) # number=29\\n prog.y(input_qubit[4]) # number=28\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[3]) # number=51\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n backend = FakeVigo()\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy1891.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=52\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[0]) # number=38\\n prog.cz(input_qubit[1],input_qubit[0]) # number=39\\n prog.h(input_qubit[0]) # number=40\\n prog.h(input_qubit[0]) # number=49\\n prog.cz(input_qubit[1],input_qubit[0]) # number=50\\n prog.h(input_qubit[0]) # number=51\\n prog.z(input_qubit[1]) # number=46\\n prog.cx(input_qubit[1],input_qubit[0]) # number=47\\n prog.h(input_qubit[0]) # number=32\\n prog.cz(input_qubit[1],input_qubit[0]) # number=33\\n prog.h(input_qubit[0]) # number=34\\n prog.x(input_qubit[4]) # number=48\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.cx(input_qubit[3],input_qubit[0]) # number=41\\n prog.z(input_qubit[3]) # number=42\\n prog.cx(input_qubit[3],input_qubit[0]) # number=43\\n prog.cx(input_qubit[1],input_qubit[3]) # number=44\\n\\n\\n prog.x(input_qubit[0]) # number=9\\n prog.x(input_qubit[1]) # number=10\\n prog.x(input_qubit[2]) # number=11\\n prog.cx(input_qubit[0],input_qubit[3]) # number=35\\n prog.x(input_qubit[3]) # number=36\\n prog.cx(input_qubit[0],input_qubit[3]) # number=37\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=24\\n prog.x(input_qubit[0]) # number=25\\n prog.cx(input_qubit[1],input_qubit[0]) # number=26\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n prog.x(input_qubit[1]) # number=22\\n prog.x(input_qubit[1]) # number=23\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n backend = FakeVigo()\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy1159.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=36\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.cx(input_qubit[0],input_qubit[3]) # number=10\\n prog.h(input_qubit[3]) # number=33\\n prog.cz(input_qubit[0],input_qubit[3]) # number=34\\n prog.h(input_qubit[3]) # number=35\\n prog.x(input_qubit[3]) # number=24\\n prog.cx(input_qubit[0],input_qubit[3]) # number=25\\n prog.cx(input_qubit[0],input_qubit[3]) # number=12\\n prog.h(input_qubit[2]) # number=30\\n prog.cz(input_qubit[0],input_qubit[2]) # number=31\\n prog.h(input_qubit[2]) # number=32\\n prog.x(input_qubit[2]) # number=21\\n prog.cx(input_qubit[0],input_qubit[2]) # number=22\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.h(input_qubit[0]) # number=5\\n prog.h(input_qubit[3]) # number=16\\n prog.cz(input_qubit[1],input_qubit[3]) # number=17\\n prog.h(input_qubit[3]) # number=18\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.h(input_qubit[0]) # number=26\\n prog.cz(input_qubit[3],input_qubit[0]) # number=27\\n prog.h(input_qubit[0]) # number=28\\n prog.cx(input_qubit[3],input_qubit[0]) # number=14\\n prog.y(input_qubit[2]) # number=29\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC1930.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=68\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\n\\ndef build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename=(kernel + '-oracle.png'))\\n return oracle\\n\\n\\ndef build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the Bernstein-Vazirani circuit\\n zero = np.binary_repr(0, n)\\n b = f(zero)\\n\\n # initial n + 1 bits\\n input_qubit = QuantumRegister(n+1, \\\"qc\\\")\\n classicals = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classicals)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(input_qubit[n])\\n # circuit begin\\n prog.h(input_qubit[1]) # number=1\\n prog.rx(-0.09738937226128368,input_qubit[2]) # number=2\\n prog.h(input_qubit[1]) # number=33\\n prog.y(input_qubit[2]) # number=56\\n prog.cz(input_qubit[2],input_qubit[1]) # number=34\\n prog.h(input_qubit[1]) # number=35\\n prog.h(input_qubit[1]) # number=3\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.h(input_qubit[n])\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [input_qubit[n]])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n return prog\\n\\n\\ndef get_statevector(prog: QuantumCircuit) -> Any:\\n state_backend = Aer.get_backend('statevector_simulator')\\n statevec = execute(prog, state_backend).result()\\n quantum_state = statevec.get_statevector()\\n qubits = round(log2(len(quantum_state)))\\n quantum_state = {\\n \\\"|\\\" + np.binary_repr(i, qubits) + \\\">\\\": quantum_state[i]\\n for i in range(2 ** qubits)\\n }\\n return quantum_state\\n\\n\\ndef evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any:\\n # Q: which backend should we use?\\n\\n # get state vector\\n quantum_state = get_statevector(prog)\\n\\n # get simulate results\\n\\n # provider = IBMQ.load_account()\\n # backend = provider.get_backend(backend_str)\\n # qobj = compile(prog, backend, shots)\\n # job = backend.run(qobj)\\n # job.result()\\n backend = Aer.get_backend(backend_str)\\n # transpile/schedule -> assemble -> backend.run\\n results = execute(prog, backend, shots=shots).result()\\n counts = results.get_counts()\\n a = Counter(counts).most_common(1)[0][0][::-1]\\n\\n return {\\n \\\"measurements\\\": counts,\\n # \\\"state\\\": statevec,\\n \\\"quantum_state\\\": quantum_state,\\n \\\"a\\\": a,\\n \\\"b\\\": b\\n }\\n\\n\\ndef bernstein_test_1(rep: str):\\n \\\"\\\"\\\"011 . x + 1\\\"\\\"\\\"\\n a = \\\"011\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_2(rep: str):\\n \\\"\\\"\\\"000 . x + 0\\\"\\\"\\\"\\n a = \\\"000\\\"\\n b = \\\"0\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_3(rep: str):\\n \\\"\\\"\\\"111 . x + 1\\\"\\\"\\\"\\n a = \\\"111\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n n = 2\\n a = \\\"11\\\"\\n b = \\\"1\\\"\\n f = lambda rep: \\\\\\n bitwise_xor(bitwise_dot(a, rep), b)\\n prog = build_circuit(n, f)\\n sample_shot =4000\\n writefile = open(\\\"../data/startQiskit_Class374.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = BasicAer.get_backend('statevector_simulator')\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.h(qubit=2)\\n circuit1.x(qubit=3)\\n\\n info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=68\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\n\\ndef build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename=(kernel + '-oracle.png'))\\n return oracle\\n\\n\\ndef build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the Bernstein-Vazirani circuit\\n zero = np.binary_repr(0, n)\\n b = f(zero)\\n\\n # initial n + 1 bits\\n input_qubit = QuantumRegister(n+1, \\\"qc\\\")\\n classicals = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classicals)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(input_qubit[n])\\n # circuit begin\\n prog.h(input_qubit[1]) # number=1\\n prog.rx(-0.09738937226128368,input_qubit[2]) # number=2\\n prog.h(input_qubit[1]) # number=33\\n prog.y(input_qubit[2]) # number=56\\n prog.cz(input_qubit[2],input_qubit[1]) # number=34\\n prog.h(input_qubit[1]) # number=35\\n prog.h(input_qubit[1]) # number=3\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.h(input_qubit[n])\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [input_qubit[n]])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n return prog\\n\\n\\ndef get_statevector(prog: QuantumCircuit) -> Any:\\n state_backend = Aer.get_backend('statevector_simulator')\\n statevec = execute(prog, state_backend).result()\\n quantum_state = statevec.get_statevector()\\n qubits = round(log2(len(quantum_state)))\\n quantum_state = {\\n \\\"|\\\" + np.binary_repr(i, qubits) + \\\">\\\": quantum_state[i]\\n for i in range(2 ** qubits)\\n }\\n return quantum_state\\n\\n\\ndef evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any:\\n # Q: which backend should we use?\\n\\n # get state vector\\n quantum_state = get_statevector(prog)\\n\\n # get simulate results\\n\\n # provider = IBMQ.load_account()\\n # backend = provider.get_backend(backend_str)\\n # qobj = compile(prog, backend, shots)\\n # job = backend.run(qobj)\\n # job.result()\\n backend = Aer.get_backend(backend_str)\\n # transpile/schedule -> assemble -> backend.run\\n results = execute(prog, backend, shots=shots).result()\\n counts = results.get_counts()\\n a = Counter(counts).most_common(1)[0][0][::-1]\\n\\n return {\\n \\\"measurements\\\": counts,\\n # \\\"state\\\": statevec,\\n \\\"quantum_state\\\": quantum_state,\\n \\\"a\\\": a,\\n \\\"b\\\": b\\n }\\n\\n\\ndef bernstein_test_1(rep: str):\\n \\\"\\\"\\\"011 . x + 1\\\"\\\"\\\"\\n a = \\\"011\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_2(rep: str):\\n \\\"\\\"\\\"000 . x + 0\\\"\\\"\\\"\\n a = \\\"000\\\"\\n b = \\\"0\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_3(rep: str):\\n \\\"\\\"\\\"111 . x + 1\\\"\\\"\\\"\\n a = \\\"111\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n n = 2\\n a = \\\"11\\\"\\n b = \\\"1\\\"\\n f = lambda rep: \\\\\\n bitwise_xor(bitwise_dot(a, rep), b)\\n prog = build_circuit(n, f)\\n sample_shot =4000\\n writefile = open(\\\"../data/startQiskit_noisy381.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = FakeYorktown()\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.h(qubit=2)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n\\n info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=2\\n# total number=15\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import IBMQ\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename='circuit/deutsch-oracle.png')\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n\\n input_qubit = QuantumRegister(n, \\\"qc\\\")\\n target = QuantumRegister(1, \\\"qt\\\")\\n prog = QuantumCircuit(input_qubit, target)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(target)\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n\\n prog.h(input_qubit[1]) # number=1\\n prog.h(target)\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [target])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n prog.y(input_qubit[1]) # number=2\\n prog.y(input_qubit[1]) # number=4\\n prog.y(input_qubit[1]) # number=3\\n prog.rx(2.0860175219836226,input_qubit[1]) # number=7\\n prog.x(input_qubit[0]) # number=5\\n prog.x(input_qubit[0]) # number=6\\n prog.h(input_qubit[0]) # number=10\\n prog.cz(input_qubit[1],input_qubit[0]) # number=11\\n prog.h(input_qubit[0]) # number=12\\n prog.cx(input_qubit[1],input_qubit[0]) # number=9\\n prog.x(input_qubit[1]) # number=13\\n prog.x(input_qubit[1]) # number=14\\n # circuit end\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n n = 2\\n f = lambda rep: rep[-1]\\n # f = lambda rep: \\\"1\\\" if rep[0:2] == \\\"01\\\" or rep[0:2] == \\\"10\\\" else \\\"0\\\"\\n # f = lambda rep: \\\"0\\\"\\n prog = make_circuit(n, f)\\n sample_shot =2800\\n backend = BasicAer.get_backend('statevector_simulator')\\n\\n circuit1 = transpile(prog,FakeVigo())\\n circuit1.x(qubit=3)\\n circuit1.x(qubit=3)\\n prog = circuit1\\n\\n\\n info = execute(prog, backend=backend).result().get_statevector()\\n qubits = round(log2(len(info)))\\n info = {\\n np.binary_repr(i, qubits): round((info[i]*(info[i].conjugate())).real,3)\\n for i in range(2 ** qubits)\\n }\\n\\n writefile = open(\\\"../data/startQiskit_Class268.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=2\\n# total number=18\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import IBMQ\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename='circuit/deutsch-oracle.png')\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n\\n input_qubit = QuantumRegister(n, \\\"qc\\\")\\n target = QuantumRegister(1, \\\"qt\\\")\\n prog = QuantumCircuit(input_qubit, target)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(target)\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n\\n prog.h(input_qubit[1]) # number=1\\n prog.h(target)\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [target])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n #for i in range(n):\\n # prog.measure(input_qubit[i], classicals[i])\\n\\n prog.y(input_qubit[1]) # number=2\\n prog.y(input_qubit[1]) # number=4\\n prog.y(input_qubit[1]) # number=3\\n prog.h(input_qubit[0]) # number=13\\n prog.cz(input_qubit[1],input_qubit[0]) # number=14\\n prog.h(input_qubit[0]) # number=15\\n prog.x(input_qubit[0]) # number=8\\n prog.cx(input_qubit[1],input_qubit[0]) # number=9\\n prog.cx(input_qubit[1],input_qubit[0]) # number=10\\n prog.x(input_qubit[0]) # number=11\\n prog.cx(input_qubit[1],input_qubit[0]) # number=12\\n prog.x(input_qubit[0]) # number=16\\n prog.x(input_qubit[0]) # number=17\\n # circuit end\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n n = 2\\n f = lambda rep: rep[-1]\\n # f = lambda rep: \\\"1\\\" if rep[0:2] == \\\"01\\\" or rep[0:2] == \\\"10\\\" else \\\"0\\\"\\n # f = lambda rep: \\\"0\\\"\\n prog = make_circuit(n, f)\\n sample_shot =2800\\n backend = FakeVigo()\\n\\n circuit1 = transpile(prog,FakeVigo())\\n circuit1.x(qubit=3)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n prog = circuit1\\n\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n\\n writefile = open(\\\"../data/startQiskit_noisy291.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=14\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\n\\ndef build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename=(kernel + '-oracle.png'))\\n return oracle\\n\\n\\ndef build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the Bernstein-Vazirani circuit\\n zero = np.binary_repr(0, n)\\n b = f(zero)\\n\\n # initial n + 1 bits\\n input_qubit = QuantumRegister(n+1, \\\"qc\\\")\\n classicals = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classicals)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(input_qubit[n])\\n # circuit begin\\n prog.h(input_qubit[1]) # number=1\\n prog.x(input_qubit[2]) # number=2\\n prog.h(input_qubit[1]) # number=7\\n prog.cz(input_qubit[2],input_qubit[1]) # number=8\\n prog.h(input_qubit[1]) # number=9\\n prog.cx(input_qubit[2],input_qubit[1]) # number=4\\n prog.h(input_qubit[1]) # number=11\\n prog.cz(input_qubit[2],input_qubit[1]) # number=12\\n prog.h(input_qubit[1]) # number=13\\n prog.z(input_qubit[2]) # number=3\\n prog.y(input_qubit[2]) # number=5\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.h(input_qubit[n])\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [input_qubit[n]])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n return prog\\n\\n\\ndef get_statevector(prog: QuantumCircuit) -> Any:\\n state_backend = Aer.get_backend('statevector_simulator')\\n statevec = execute(prog, state_backend).result()\\n quantum_state = statevec.get_statevector()\\n qubits = round(log2(len(quantum_state)))\\n quantum_state = {\\n \\\"|\\\" + np.binary_repr(i, qubits) + \\\">\\\": quantum_state[i]\\n for i in range(2 ** qubits)\\n }\\n return quantum_state\\n\\n\\ndef evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any:\\n # Q: which backend should we use?\\n\\n # get state vector\\n quantum_state = get_statevector(prog)\\n\\n # get simulate results\\n\\n # provider = IBMQ.load_account()\\n # backend = provider.get_backend(backend_str)\\n # qobj = compile(prog, backend, shots)\\n # job = backend.run(qobj)\\n # job.result()\\n backend = Aer.get_backend(backend_str)\\n # transpile/schedule -> assemble -> backend.run\\n results = execute(prog, backend, shots=shots).result()\\n counts = results.get_counts()\\n a = Counter(counts).most_common(1)[0][0][::-1]\\n\\n return {\\n \\\"measurements\\\": counts,\\n # \\\"state\\\": statevec,\\n \\\"quantum_state\\\": quantum_state,\\n \\\"a\\\": a,\\n \\\"b\\\": b\\n }\\n\\n\\ndef bernstein_test_1(rep: str):\\n \\\"\\\"\\\"011 . x + 1\\\"\\\"\\\"\\n a = \\\"011\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_2(rep: str):\\n \\\"\\\"\\\"000 . x + 0\\\"\\\"\\\"\\n a = \\\"000\\\"\\n b = \\\"0\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_3(rep: str):\\n \\\"\\\"\\\"111 . x + 1\\\"\\\"\\\"\\n a = \\\"111\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n n = 2\\n a = \\\"11\\\"\\n b = \\\"1\\\"\\n f = lambda rep: \\\\\\n bitwise_xor(bitwise_dot(a, rep), b)\\n prog = build_circuit(n, f)\\n sample_shot =4000\\n writefile = open(\\\"../data/startQiskit81.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = BasicAer.get_backend('qasm_simulator')\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.h(qubit=2)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n\\n info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=14\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nimport networkx as nx\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef make_circuit(n:int) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n prog = QuantumCircuit(input_qubit)\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.x(input_qubit[2]) # number=6\\n prog.y(input_qubit[3]) # number=5\\n\\n for edge in E:\\n k = edge[0]\\n l = edge[1]\\n prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1])\\n prog.p(gamma, k)\\n prog.p(gamma, l)\\n\\n prog.rx(2 * beta, range(len(V)))\\n\\n prog.swap(input_qubit[1],input_qubit[0]) # number=7\\n prog.h(input_qubit[1]) # number=11\\n prog.swap(input_qubit[1],input_qubit[0]) # number=8\\n prog.y(input_qubit[0]) # number=9\\n prog.y(input_qubit[0]) # number=10\\n prog.x(input_qubit[1]) # number=12\\n prog.x(input_qubit[1]) # number=13\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n n = 4\\n V = np.arange(0, n, 1)\\n E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]\\n\\n G = nx.Graph()\\n G.add_nodes_from(V)\\n G.add_weighted_edges_from(E)\\n\\n step_size = 0.1\\n\\n a_gamma = np.arange(0, np.pi, step_size)\\n a_beta = np.arange(0, np.pi, step_size)\\n a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)\\n\\n F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (\\n 1 + np.cos(4 * a_gamma) ** 2)\\n\\n result = np.where(F1 == np.amax(F1))\\n a = list(zip(result[0], result[1]))[0]\\n\\n gamma = a[0] * step_size\\n beta = a[1] * step_size\\n\\n prog = make_circuit(4)\\n sample_shot =3962\\n writefile = open(\\\"../data/startQiskit_QC730.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = provider.get_backend(\\\"ibmq_5_yorktown\\\")\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.measure_all()\\n prog = circuit1\\n\\n info = execute(prog,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=2\\n# total number=12\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import IBMQ\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename='circuit/deutsch-oracle.png')\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n\\n input_qubit = QuantumRegister(n, \\\"qc\\\")\\n target = QuantumRegister(1, \\\"qt\\\")\\n prog = QuantumCircuit(input_qubit, target)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(target)\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n\\n prog.h(input_qubit[1]) # number=1\\n prog.cx(input_qubit[0],input_qubit[1]) # number=6\\n prog.x(input_qubit[1]) # number=7\\n prog.cx(input_qubit[0],input_qubit[1]) # number=8\\n prog.cx(input_qubit[0],input_qubit[1]) # number=4\\n prog.h(target)\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [target])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n #for i in range(n):\\n # prog.measure(input_qubit[i], classicals[i])\\n\\n prog.cx(input_qubit[0],input_qubit[1]) # number=9\\n prog.x(input_qubit[1]) # number=10\\n prog.cx(input_qubit[0],input_qubit[1]) # number=11\\n prog.x(input_qubit[1]) # number=3\\n # circuit end\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n n = 2\\n f = lambda rep: rep[-1]\\n # f = lambda rep: \\\"1\\\" if rep[0:2] == \\\"01\\\" or rep[0:2] == \\\"10\\\" else \\\"0\\\"\\n # f = lambda rep: \\\"0\\\"\\n prog = make_circuit(n, f)\\n sample_shot =2800\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = provider.get_backend(\\\"ibmq_belem\\\")\\n\\n circuit1 = transpile(prog,FakeVigo())\\n circuit1.x(qubit=3)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n prog = circuit1\\n\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n\\n writefile = open(\\\"../data/startQiskit_QC170.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=36\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[3]) # number=30\\n prog.cz(input_qubit[0],input_qubit[3]) # number=31\\n prog.h(input_qubit[3]) # number=32\\n prog.cx(input_qubit[0],input_qubit[3]) # number=33\\n prog.x(input_qubit[3]) # number=34\\n prog.cx(input_qubit[0],input_qubit[3]) # number=35\\n prog.cx(input_qubit[0],input_qubit[3]) # number=29\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=12\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[2]) # number=10\\n prog.y(input_qubit[2]) # number=11\\n prog.cx(input_qubit[1],input_qubit[0]) # number=13\\n prog.h(input_qubit[0]) # number=15\\n prog.cz(input_qubit[1],input_qubit[0]) # number=16\\n prog.h(input_qubit[1]) # number=20\\n prog.h(input_qubit[2]) # number=19\\n prog.cx(input_qubit[3],input_qubit[0]) # number=24\\n prog.z(input_qubit[3]) # number=25\\n prog.cx(input_qubit[3],input_qubit[0]) # number=26\\n prog.h(input_qubit[0]) # number=17\\n prog.cx(input_qubit[2],input_qubit[0]) # number=21\\n prog.x(input_qubit[1]) # number=23\\n prog.cx(input_qubit[2],input_qubit[0]) # number=22\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = FakeVigo()\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy2573.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=77\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\n\\ndef build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n # oracle.draw('mpl', filename=(kernel + '-oracle.png'))\\n return oracle\\n\\n\\ndef build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit:\\n # implement the Bernstein-Vazirani circuit\\n zero = np.binary_repr(0, n)\\n b = f(zero)\\n\\n # initial n + 1 bits\\n input_qubit = QuantumRegister(n+1, \\\"qc\\\")\\n classicals = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classicals)\\n\\n # inverse last one (can be omitted if using O_f^\\\\pm)\\n prog.x(input_qubit[n])\\n # circuit begin\\n prog.h(input_qubit[1]) # number=1\\n prog.h(input_qubit[2]) # number=38\\n prog.cz(input_qubit[0],input_qubit[2]) # number=39\\n prog.h(input_qubit[2]) # number=40\\n prog.h(input_qubit[2]) # number=59\\n prog.cz(input_qubit[0],input_qubit[2]) # number=60\\n prog.h(input_qubit[2]) # number=61\\n prog.h(input_qubit[2]) # number=42\\n prog.cz(input_qubit[0],input_qubit[2]) # number=43\\n prog.h(input_qubit[2]) # number=44\\n prog.h(input_qubit[2]) # number=48\\n prog.cz(input_qubit[0],input_qubit[2]) # number=49\\n prog.h(input_qubit[2]) # number=50\\n prog.h(input_qubit[2]) # number=71\\n prog.cz(input_qubit[0],input_qubit[2]) # number=72\\n prog.h(input_qubit[2]) # number=73\\n prog.x(input_qubit[2]) # number=55\\n prog.h(input_qubit[2]) # number=67\\n prog.cz(input_qubit[0],input_qubit[2]) # number=68\\n prog.h(input_qubit[2]) # number=69\\n prog.h(input_qubit[2]) # number=64\\n prog.cz(input_qubit[0],input_qubit[2]) # number=65\\n prog.h(input_qubit[2]) # number=66\\n prog.cx(input_qubit[0],input_qubit[2]) # number=37\\n prog.h(input_qubit[2]) # number=51\\n prog.cz(input_qubit[0],input_qubit[2]) # number=52\\n prog.h(input_qubit[2]) # number=53\\n prog.h(input_qubit[2]) # number=25\\n prog.cz(input_qubit[0],input_qubit[2]) # number=26\\n prog.h(input_qubit[2]) # number=27\\n prog.h(input_qubit[1]) # number=7\\n prog.cz(input_qubit[2],input_qubit[1]) # number=8\\n prog.rx(0.17592918860102857,input_qubit[2]) # number=34\\n prog.rx(-0.3989822670059037,input_qubit[1]) # number=30\\n prog.h(input_qubit[1]) # number=9\\n prog.h(input_qubit[1]) # number=18\\n prog.rx(2.3310617489636263,input_qubit[2]) # number=58\\n prog.cz(input_qubit[2],input_qubit[1]) # number=19\\n prog.h(input_qubit[1]) # number=20\\n prog.x(input_qubit[1]) # number=62\\n prog.y(input_qubit[1]) # number=14\\n prog.h(input_qubit[1]) # number=22\\n prog.cz(input_qubit[2],input_qubit[1]) # number=23\\n prog.rx(-0.9173450548482197,input_qubit[1]) # number=57\\n prog.cx(input_qubit[2],input_qubit[1]) # number=63\\n prog.h(input_qubit[1]) # number=24\\n prog.cx(input_qubit[2],input_qubit[0]) # number=74\\n prog.z(input_qubit[2]) # number=75\\n prog.cx(input_qubit[2],input_qubit[0]) # number=76\\n prog.cx(input_qubit[2],input_qubit[1]) # number=70\\n prog.z(input_qubit[1]) # number=41\\n prog.x(input_qubit[1]) # number=17\\n prog.y(input_qubit[2]) # number=5\\n prog.x(input_qubit[2]) # number=21\\n\\n # apply H to get superposition\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.h(input_qubit[n])\\n prog.barrier()\\n\\n # apply oracle O_f\\n oracle = build_oracle(n, f)\\n prog.append(\\n oracle.to_gate(),\\n [input_qubit[i] for i in range(n)] + [input_qubit[n]])\\n\\n # apply H back (QFT on Z_2^n)\\n for i in range(n):\\n prog.h(input_qubit[i])\\n prog.barrier()\\n\\n # measure\\n\\n return prog\\n\\n\\ndef get_statevector(prog: QuantumCircuit) -> Any:\\n state_backend = Aer.get_backend('statevector_simulator')\\n statevec = execute(prog, state_backend).result()\\n quantum_state = statevec.get_statevector()\\n qubits = round(log2(len(quantum_state)))\\n quantum_state = {\\n \\\"|\\\" + np.binary_repr(i, qubits) + \\\">\\\": quantum_state[i]\\n for i in range(2 ** qubits)\\n }\\n return quantum_state\\n\\n\\ndef evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any:\\n # Q: which backend should we use?\\n\\n # get state vector\\n quantum_state = get_statevector(prog)\\n\\n # get simulate results\\n\\n # provider = IBMQ.load_account()\\n # backend = provider.get_backend(backend_str)\\n # qobj = compile(prog, backend, shots)\\n # job = backend.run(qobj)\\n # job.result()\\n backend = Aer.get_backend(backend_str)\\n # transpile/schedule -> assemble -> backend.run\\n results = execute(prog, backend, shots=shots).result()\\n counts = results.get_counts()\\n a = Counter(counts).most_common(1)[0][0][::-1]\\n\\n return {\\n \\\"measurements\\\": counts,\\n # \\\"state\\\": statevec,\\n \\\"quantum_state\\\": quantum_state,\\n \\\"a\\\": a,\\n \\\"b\\\": b\\n }\\n\\n\\ndef bernstein_test_1(rep: str):\\n \\\"\\\"\\\"011 . x + 1\\\"\\\"\\\"\\n a = \\\"011\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_2(rep: str):\\n \\\"\\\"\\\"000 . x + 0\\\"\\\"\\\"\\n a = \\\"000\\\"\\n b = \\\"0\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\ndef bernstein_test_3(rep: str):\\n \\\"\\\"\\\"111 . x + 1\\\"\\\"\\\"\\n a = \\\"111\\\"\\n b = \\\"1\\\"\\n return bitwise_xor(bitwise_dot(a, rep), b)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n n = 2\\n a = \\\"11\\\"\\n b = \\\"1\\\"\\n f = lambda rep: \\\\\\n bitwise_xor(bitwise_dot(a, rep), b)\\n prog = build_circuit(n, f)\\n sample_shot =4000\\n writefile = open(\\\"../data/startQiskit_noisy387.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = FakeYorktown()\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.h(qubit=2)\\n circuit1.x(qubit=3)\\n circuit1.measure_all()\\n\\n info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=4\\n# total number=33\\nimport cirq\\nimport qiskit\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2\\nimport numpy as np\\nimport networkx as nx\\n\\ndef bitwise_xor(s: str, t: str) -> str:\\n length = len(s)\\n res = []\\n for i in range(length):\\n res.append(str(int(s[i]) ^ int(t[i])))\\n return ''.join(res[::-1])\\n\\n\\ndef bitwise_dot(s: str, t: str) -> str:\\n length = len(s)\\n res = 0\\n for i in range(length):\\n res += int(s[i]) * int(t[i])\\n return str(res % 2)\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f\\n # NOTE: use multi_control_toffoli_gate ('noancilla' mode)\\n # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html\\n # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates\\n # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n target = QuantumRegister(1, \\\"oft\\\")\\n oracle = QuantumCircuit(controls, target, name=\\\"Of\\\")\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n oracle.mct(controls, target[0], None, mode='noancilla')\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n return oracle\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.cx(input_qubit[0],input_qubit[3]) # number=27\\n prog.cx(input_qubit[0],input_qubit[3]) # number=30\\n prog.x(input_qubit[3]) # number=31\\n prog.cx(input_qubit[0],input_qubit[3]) # number=32\\n prog.cx(input_qubit[0],input_qubit[3]) # number=29\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=12\\n prog.h(input_qubit[0]) # number=5\\n\\n oracle = build_oracle(n-1, f)\\n prog.append(oracle.to_gate(),[input_qubit[i] for i in range(n-1)]+[input_qubit[n-1]])\\n prog.h(input_qubit[1]) # number=6\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=9\\n\\n prog.y(input_qubit[2]) # number=10\\n prog.y(input_qubit[2]) # number=11\\n prog.cx(input_qubit[1],input_qubit[0]) # number=13\\n prog.h(input_qubit[0]) # number=15\\n prog.cz(input_qubit[1],input_qubit[0]) # number=16\\n prog.h(input_qubit[1]) # number=20\\n prog.h(input_qubit[2]) # number=19\\n prog.cx(input_qubit[3],input_qubit[0]) # number=24\\n prog.z(input_qubit[3]) # number=25\\n prog.cx(input_qubit[3],input_qubit[0]) # number=26\\n prog.h(input_qubit[0]) # number=17\\n prog.cx(input_qubit[2],input_qubit[0]) # number=21\\n prog.x(input_qubit[1]) # number=23\\n prog.cx(input_qubit[2],input_qubit[0]) # number=22\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n a = \\\"111\\\"\\n b = \\\"0\\\"\\n f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)\\n prog = make_circuit(4,f)\\n backend = BasicAer.get_backend('statevector_simulator')\\n sample_shot =8000\\n\\n info = execute(prog, backend=backend).result().get_statevector()\\n qubits = round(log2(len(info)))\\n info = {\\n np.binary_repr(i, qubits): round((info[i]*(info[i].conjugate())).real,3)\\n for i in range(2 ** qubits)\\n }\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_Class2317.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.__len__(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=3\\n# total number=12\\n\\nimport numpy as np\\n\\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\\nimport networkx as nx\\nfrom qiskit.visualization import plot_histogram\\nfrom typing import *\\nfrom pprint import pprint\\nfrom math import log2\\nfrom collections import Counter\\nfrom qiskit.test.mock import FakeVigo, FakeYorktown\\n\\nkernel = 'circuit/bernstein'\\n\\n\\ndef make_circuit(n:int) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n prog = QuantumCircuit(input_qubit)\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=3\\n prog.h(input_qubit[3]) # number=4\\n prog.y(input_qubit[3]) # number=5\\n\\n for edge in E:\\n k = edge[0]\\n l = edge[1]\\n prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1])\\n prog.p(gamma, k)\\n prog.p(gamma, l)\\n\\n prog.rx(2 * beta, range(len(V)))\\n\\n prog.swap(input_qubit[1],input_qubit[0]) # number=6\\n prog.swap(input_qubit[1],input_qubit[0]) # number=7\\n prog.x(input_qubit[0]) # number=8\\n prog.x(input_qubit[0]) # number=9\\n prog.y(input_qubit[1]) # number=10\\n prog.y(input_qubit[1]) # number=11\\n # circuit end\\n\\n\\n\\n return prog\\n\\n\\n\\nif __name__ == '__main__':\\n n = 4\\n V = np.arange(0, n, 1)\\n E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]\\n\\n G = nx.Graph()\\n G.add_nodes_from(V)\\n G.add_weighted_edges_from(E)\\n\\n step_size = 0.1\\n\\n a_gamma = np.arange(0, np.pi, step_size)\\n a_beta = np.arange(0, np.pi, step_size)\\n a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)\\n\\n F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (\\n 1 + np.cos(4 * a_gamma) ** 2)\\n\\n result = np.where(F1 == np.amax(F1))\\n a = list(zip(result[0], result[1]))[0]\\n\\n gamma = a[0] * step_size\\n beta = a[1] * step_size\\n\\n prog = make_circuit(4)\\n sample_shot =3962\\n writefile = open(\\\"../data/startQiskit_noisy343.csv\\\", \\\"w\\\")\\n # prog.draw('mpl', filename=(kernel + '.png'))\\n backend = FakeYorktown()\\n\\n circuit1 = transpile(prog, FakeYorktown())\\n circuit1.measure_all()\\n prog = circuit1\\n\\n info = execute(prog,backend=backend, shots=sample_shot).result().get_counts()\\n\\n print(info, file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(), file=writefile)\\n print(circuit1, file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=49\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[1]) # number=26\\n prog.cz(input_qubit[4],input_qubit[1]) # number=27\\n prog.h(input_qubit[1]) # number=28\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[4]) # number=21\\n prog.h(input_qubit[1]) # number=34\\n prog.cz(input_qubit[4],input_qubit[1]) # number=35\\n prog.rx(0.8011061266653969,input_qubit[2]) # number=37\\n prog.h(input_qubit[1]) # number=36\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=38\\n prog.x(input_qubit[0]) # number=39\\n prog.cx(input_qubit[1],input_qubit[0]) # number=40\\n prog.h(input_qubit[1]) # number=46\\n prog.cz(input_qubit[0],input_qubit[1]) # number=47\\n prog.h(input_qubit[1]) # number=48\\n prog.x(input_qubit[1]) # number=43\\n prog.cx(input_qubit[0],input_qubit[1]) # number=44\\n prog.x(input_qubit[2]) # number=11\\n prog.y(input_qubit[1]) # number=45\\n prog.x(input_qubit[3]) # number=12\\n prog.h(input_qubit[2]) # number=41\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=22\\n prog.x(input_qubit[0]) # number=23\\n prog.cx(input_qubit[1],input_qubit[0]) # number=24\\n prog.cx(input_qubit[0],input_qubit[1]) # number=30\\n prog.x(input_qubit[1]) # number=31\\n prog.cx(input_qubit[0],input_qubit[1]) # number=32\\n prog.x(input_qubit[2]) # number=15\\n prog.h(input_qubit[4]) # number=29\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC1221.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=50\\nimport cirq\\nimport qiskit\\nfrom qiskit import IBMQ\\nfrom qiskit.providers.ibmq import least_busy\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.rx(-1.3603096190043806,input_qubit[2]) # number=28\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[4]) # number=21\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[3]) # number=34\\n prog.cz(input_qubit[4],input_qubit[3]) # number=35\\n prog.h(input_qubit[3]) # number=36\\n\\n\\n prog.h(input_qubit[0]) # number=38\\n prog.cz(input_qubit[1],input_qubit[0]) # number=39\\n prog.h(input_qubit[0]) # number=40\\n prog.x(input_qubit[0]) # number=32\\n prog.cx(input_qubit[1],input_qubit[0]) # number=33\\n prog.cx(input_qubit[0],input_qubit[1]) # number=24\\n prog.x(input_qubit[1]) # number=25\\n prog.x(input_qubit[1]) # number=41\\n prog.cx(input_qubit[0],input_qubit[1]) # number=26\\n prog.x(input_qubit[2]) # number=11\\n prog.cx(input_qubit[2],input_qubit[3]) # number=30\\n prog.x(input_qubit[3]) # number=12\\n prog.h(input_qubit[2]) # number=42\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.x(input_qubit[0]) # number=13\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[4]) # number=46\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.cx(input_qubit[0],input_qubit[2]) # number=43\\n prog.cx(input_qubit[0],input_qubit[2]) # number=47\\n prog.x(input_qubit[2]) # number=48\\n prog.cx(input_qubit[0],input_qubit[2]) # number=49\\n prog.cx(input_qubit[0],input_qubit[2]) # number=45\\n prog.rx(-1.9697785938008003,input_qubit[1]) # number=37\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n prog.x(input_qubit[1]) # number=22\\n prog.x(input_qubit[1]) # number=23\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n IBMQ.load_account() \\n provider = IBMQ.get_provider(hub='ibm-q') \\n provider.backends()\\n backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_QC1367.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\",\n \"# qubit number=5\\n# total number=62\\nimport cirq\\nimport qiskit\\nfrom qiskit.providers.aer import QasmSimulator\\nfrom qiskit.test.mock import FakeVigo\\n\\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\nfrom qiskit import BasicAer, execute, transpile\\nfrom pprint import pprint\\nfrom qiskit.test.mock import FakeVigo\\nfrom math import log2,floor, sqrt, pi\\nimport numpy as np\\nimport networkx as nx\\n\\ndef build_oracle(n: int, f) -> QuantumCircuit:\\n # implement the oracle O_f^\\\\pm\\n # NOTE: use U1 gate (P gate) with \\\\lambda = 180 ==> CZ gate\\n # or multi_control_Z_gate (issue #127)\\n\\n controls = QuantumRegister(n, \\\"ofc\\\")\\n oracle = QuantumCircuit(controls, name=\\\"Zf\\\")\\n\\n for i in range(2 ** n):\\n rep = np.binary_repr(i, n)\\n if f(rep) == \\\"1\\\":\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n\\n # oracle.h(controls[n])\\n if n >= 2:\\n oracle.mcu1(pi, controls[1:], controls[0])\\n\\n for j in range(n):\\n if rep[j] == \\\"0\\\":\\n oracle.x(controls[j])\\n # oracle.barrier()\\n\\n return oracle\\n\\n\\ndef make_circuit(n:int,f) -> QuantumCircuit:\\n # circuit begin\\n input_qubit = QuantumRegister(n,\\\"qc\\\")\\n classical = ClassicalRegister(n, \\\"qm\\\")\\n prog = QuantumCircuit(input_qubit, classical)\\n prog.h(input_qubit[0]) # number=3\\n prog.h(input_qubit[1]) # number=4\\n prog.h(input_qubit[2]) # number=5\\n prog.h(input_qubit[3]) # number=6\\n prog.h(input_qubit[0]) # number=41\\n prog.cz(input_qubit[1],input_qubit[0]) # number=42\\n prog.h(input_qubit[0]) # number=43\\n prog.z(input_qubit[1]) # number=37\\n prog.h(input_qubit[0]) # number=51\\n prog.cz(input_qubit[1],input_qubit[0]) # number=52\\n prog.h(input_qubit[0]) # number=53\\n prog.h(input_qubit[4]) # number=21\\n prog.x(input_qubit[2]) # number=39\\n\\n Zf = build_oracle(n, f)\\n\\n repeat = floor(sqrt(2 ** n) * pi / 4)\\n for i in range(repeat):\\n prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])\\n prog.h(input_qubit[0]) # number=1\\n prog.h(input_qubit[1]) # number=2\\n prog.h(input_qubit[2]) # number=7\\n prog.h(input_qubit[3]) # number=8\\n prog.h(input_qubit[0]) # number=56\\n prog.cz(input_qubit[3],input_qubit[0]) # number=57\\n prog.h(input_qubit[0]) # number=58\\n prog.h(input_qubit[0]) # number=48\\n prog.cz(input_qubit[3],input_qubit[0]) # number=49\\n prog.h(input_qubit[0]) # number=50\\n prog.z(input_qubit[3]) # number=46\\n prog.h(input_qubit[0]) # number=59\\n prog.cz(input_qubit[3],input_qubit[0]) # number=60\\n prog.h(input_qubit[0]) # number=61\\n prog.x(input_qubit[4]) # number=40\\n prog.cx(input_qubit[3],input_qubit[0]) # number=35\\n\\n\\n prog.x(input_qubit[0]) # number=9\\n prog.cx(input_qubit[0],input_qubit[1]) # number=29\\n prog.x(input_qubit[1]) # number=30\\n prog.cx(input_qubit[0],input_qubit[1]) # number=31\\n prog.x(input_qubit[2]) # number=11\\n prog.x(input_qubit[1]) # number=44\\n prog.x(input_qubit[3]) # number=12\\n\\n if n>=2:\\n prog.mcu1(pi,input_qubit[1:],input_qubit[0])\\n\\n prog.cx(input_qubit[1],input_qubit[0]) # number=24\\n prog.x(input_qubit[0]) # number=25\\n prog.cx(input_qubit[1],input_qubit[0]) # number=26\\n prog.x(input_qubit[1]) # number=14\\n prog.x(input_qubit[2]) # number=15\\n prog.x(input_qubit[3]) # number=16\\n\\n\\n prog.h(input_qubit[0]) # number=17\\n prog.h(input_qubit[1]) # number=18\\n prog.cx(input_qubit[4],input_qubit[3]) # number=54\\n prog.h(input_qubit[2]) # number=19\\n prog.h(input_qubit[3]) # number=20\\n\\n\\n prog.x(input_qubit[1]) # number=22\\n prog.y(input_qubit[1]) # number=32\\n prog.x(input_qubit[1]) # number=23\\n prog.cx(input_qubit[4],input_qubit[3]) # number=55\\n # circuit end\\n\\n for i in range(n):\\n prog.measure(input_qubit[i], classical[i])\\n\\n\\n return prog\\n\\n\\n\\n\\nif __name__ == '__main__':\\n key = \\\"00000\\\"\\n f = lambda rep: str(int(rep == key))\\n prog = make_circuit(5,f)\\n backend = FakeVigo()\\n sample_shot =7924\\n\\n info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()\\n backend = FakeVigo()\\n circuit1 = transpile(prog,backend,optimization_level=2)\\n\\n writefile = open(\\\"../data/startQiskit_noisy1857.csv\\\",\\\"w\\\")\\n print(info,file=writefile)\\n print(\\\"results end\\\", file=writefile)\\n print(circuit1.depth(),file=writefile)\\n print(circuit1,file=writefile)\\n writefile.close()\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr","numpy.identity"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.amax","numpy.arange","numpy.cos","numpy.sin","numpy.meshgrid"],["numpy.binary_repr"],["numpy.binary_repr"],["numpy.binary_repr"]],"string":"[\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\",\n \"numpy.identity\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.amax\",\n \"numpy.arange\",\n \"numpy.cos\",\n \"numpy.sin\",\n \"numpy.meshgrid\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ],\n [\n \"numpy.binary_repr\"\n ]\n]","isConversational":false}}},{"rowIdx":1288,"cells":{"repo_name":{"kind":"string","value":"JoZimmer/ParOptBeam"},"hexsha":{"kind":"list like","value":["50d15d8d822a2718f2932807e06c4a7e02f866a3"],"string":"[\n \"50d15d8d822a2718f2932807e06c4a7e02f866a3\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["test_scripts/test_element_and_compare_to_kratos.py"],"string":"[\n \"test_scripts/test_element_and_compare_to_kratos.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["from source.element.cr_beam_element import CRBeamElement\n\nimport numpy as np\n\nnp.set_printoptions(suppress=False, precision=4, linewidth=100)\n\n\ndef test_crbeam_element_update_incremental():\n material_params = {'rho': 7850, 'e': 2069000000.0, 'nu': 0.29, 'zeta': 0.05, 'lx_i': 1.2, 'is_nonlinear': True}\n element_params = {'a': 0.0001, 'asy': 0.0, 'asz': 0.0, 'iy': 0.0001, 'iz': 0.0001, 'it': 0.0001}\n\n coords = np.array([[1.2, 0.0, 0.0], [0.0, 0.0, 0.0]])\n element = CRBeamElement(material_params, element_params, coords, 0, '3D')\n\n Kd_kratos = np.array([\n [66828.2, 0, 0, 0, 0, 0],\n [0, 172417, 0, 0, 0, 0],\n [0, 0, 172417, 0, 0, 0],\n [0, 0, 0, 172417, 0, 0],\n [0, 0, 0, 0, 517250, 0],\n [0, 0, 0, 0, 0, 517250]\n ])\n\n Kd = element.Kd_mat\n\n try:\n assert (abs(Kd_kratos - Kd) < 10).all()\n except AssertionError:\n msg = \"##################################################################################\\n\"\n msg += \"Deformation Stiffness matrix\\n\"\n msg += \"Kd in Kratos:\\n\" + str(Kd_kratos)\n msg += \"\\nIt is however:\\n\" + str(Kd)\n print(msg)\n\n Ke_mat_kratos = np.array([\n [172417, 0, 0, 0, 0, 0, -172417, 0, 0, 0, 0, 0],\n [0, 1.43681e+06, 0, 0, 0, 862083, 0, -1.43681e+06, 0, 0, 0, 862083],\n [0, 0, 1.43681e+06, 0, -862083, 0, 0, 0, -1.43681e+06, 0, -862083, 0],\n [0, 0, 0, 66828.2, 0, 0, 0, 0, 0, -66828.2, 0, 0],\n [0, 0, -862083, 0, 689667, 0, 0, 0, 862083, 0, 344833, 0],\n [0, 862083, 0, 0, 0, 689667, 0, -862083, 0, 0, 0, 344833],\n [-172417, 0, 0, 0, 0, 0, 172417, 0, 0, 0, 0, 0],\n [0, -1.43681e+06, 0, 0, 0, -862083, 0, 1.43681e+06, 0, 0, 0, -862083],\n [0, 0, -1.43681e+06, 0, 862083, 0, 0, 0, 1.43681e+06, 0, 862083, 0],\n [0, 0, 0, -66828.2, 0, 0, 0, 0, 0, 66828.2, 0, 0],\n [0, 0, -862083, 0, 344833, 0, 0, 0, 862083, 0, 689667, 0],\n [0, 862083, 0, 0, 0, 344833, 0, -862083, 0, 0, 0, 689667]\n ])\n\n Ke_mat = element.Ke_mat\n\n try:\n assert (abs(Ke_mat_kratos - Ke_mat) < 10).all()\n except AssertionError:\n msg = \"##################################################################################\\n\"\n msg += \"Material Stiffness matrix\\n\"\n msg += \"Ke_mat in Kratos:\\n\" + str(Ke_mat_kratos)\n msg += \"\\nIt is however:\\n\" + str(Ke_mat)\n print(msg)\n\n Phiz = 0.0\n Phiy = 0.0\n\n CTy = (element.rho * element.A * element.L) / ((1 + Phiy) * (1 + Phiy))\n CTz = (element.rho * element.A * element.L) / ((1 + Phiz) * (1 + Phiz))\n\n CRy = (element.rho * element.Iy) / ((1 + Phiy) * (1 + Phiy) * element.L)\n CRz = (element.rho * element.Iz) / ((1 + Phiz) * (1 + Phiz) * element.L)\n\n bending_mass_matrix_z = element.build_single_mass_matrix(Phiz, CTz, CRz, element.L, +1)\n\n bending_mass_matrix_kratos_z = np.array([\n [1.13489, 0.137711, -0.663886, 0.0435114],\n [0.137711, 0.138519, -0.0435114, -0.0410891],\n [-0.663886, -0.0435114, 1.13489, -0.137711],\n [0.0435114, -0.0410891, -0.137711, 0.138519]\n ])\n\n try:\n assert (abs(bending_mass_matrix_z - bending_mass_matrix_kratos_z) < 1e-4).all()\n print(\"Bending mass_matrix z is correct\")\n except AssertionError:\n msg = \"##################################################################################\\n\"\n msg += \"Bending mass matrix z\\n\"\n msg += \"Me in Kratos:\\n\" + str(bending_mass_matrix_kratos_z)\n msg += \"\\nIt is however:\\n\" + str(bending_mass_matrix_z)\n print(msg)\n\n bending_mass_matrix_y = element.build_single_mass_matrix(Phiz, CTy, CRy, element.L, -1)\n\n bending_mass_matrix_kratos_y = np.array([\n [1.13489, -0.137711, -0.663886, -0.0435114],\n [-0.137711, 0.138519, 0.0435114, -0.0410891],\n [-0.663886, 0.0435114, 1.13489, 0.137711],\n [-0.0435114, -0.0410891, 0.137711, 0.138519]\n ])\n\n try:\n assert (abs(bending_mass_matrix_y - bending_mass_matrix_kratos_y) < 1e-4).all()\n print(\"Bending mass_matrix y is correct\")\n except AssertionError:\n msg = \"##################################################################################\\n\"\n msg += \"Bending mass matrix y\\n\"\n msg += \"Me in Kratos:\\n\" + str(bending_mass_matrix_kratos_y)\n msg += \"\\nIt is however:\\n\" + str(bending_mass_matrix_y)\n print(msg)\n\n Me = element._get_consistent_mass_matrix()\n\n Me_kratos = np.array([\n [0.314, 0, 0, 0, 0, 0, 0.157, 0, 0, 0, 0, 0],\n [0, 1.13489, 0, 0, 0, 0.137711, 0, -0.663886, 0, 0, 0, 0.0435114],\n [0, 0, 1.13489, 0, -0.137711, 0, 0, 0, -0.663886, 0, -0.0435114, 0],\n [0, 0, 0, 0.628, 0, 0, 0, 0, 0, 0.314, 0, 0],\n [0, 0, -0.137711, 0, 0.138519, 0, 0, 0, 0.0435114, 0, -0.0410891, 0],\n [0, 0.137711, 0, 0, 0, 0.138519, 0, -0.0435114, 0, 0, 0, -0.0410891],\n [0.157, 0, 0, 0, 0, 0, 0.314, 0, 0, 0, 0, 0],\n [0, -0.663886, 0, 0, 0, -0.0435114, 0, 1.13489, 0, 0, 0, -0.137711],\n [0, 0, -0.663886, 0, 0.0435114, 0, 0, 0, 1.13489, 0, 0.137711, 0],\n [0, 0, 0, 0.314, 0, 0, 0, 0, 0, 0.628, 0, 0],\n [0, 0, -0.0435114, 0, -0.0410891, 0, 0, 0, 0.137711, 0, 0.138519, 0],\n [0, 0.0435114, 0, 0, 0, -0.0410891, 0, -0.137711, 0, 0, 0, 0.138519]\n ])\n\n try:\n assert (abs(Me - Me_kratos) < 1e-2).all()\n print(\"Mass matrix is correct\")\n except AssertionError:\n msg = \"##################################################################################\\n\"\n msg += \"Consistent mass matrix\\n\"\n msg += \"Me in Kratos:\\n\" + str(Me_kratos)\n msg += \"\\nIt is however:\\n\" + str(Me)\n print(msg)\n"],"string":"[\n \"from source.element.cr_beam_element import CRBeamElement\\n\\nimport numpy as np\\n\\nnp.set_printoptions(suppress=False, precision=4, linewidth=100)\\n\\n\\ndef test_crbeam_element_update_incremental():\\n material_params = {'rho': 7850, 'e': 2069000000.0, 'nu': 0.29, 'zeta': 0.05, 'lx_i': 1.2, 'is_nonlinear': True}\\n element_params = {'a': 0.0001, 'asy': 0.0, 'asz': 0.0, 'iy': 0.0001, 'iz': 0.0001, 'it': 0.0001}\\n\\n coords = np.array([[1.2, 0.0, 0.0], [0.0, 0.0, 0.0]])\\n element = CRBeamElement(material_params, element_params, coords, 0, '3D')\\n\\n Kd_kratos = np.array([\\n [66828.2, 0, 0, 0, 0, 0],\\n [0, 172417, 0, 0, 0, 0],\\n [0, 0, 172417, 0, 0, 0],\\n [0, 0, 0, 172417, 0, 0],\\n [0, 0, 0, 0, 517250, 0],\\n [0, 0, 0, 0, 0, 517250]\\n ])\\n\\n Kd = element.Kd_mat\\n\\n try:\\n assert (abs(Kd_kratos - Kd) < 10).all()\\n except AssertionError:\\n msg = \\\"##################################################################################\\\\n\\\"\\n msg += \\\"Deformation Stiffness matrix\\\\n\\\"\\n msg += \\\"Kd in Kratos:\\\\n\\\" + str(Kd_kratos)\\n msg += \\\"\\\\nIt is however:\\\\n\\\" + str(Kd)\\n print(msg)\\n\\n Ke_mat_kratos = np.array([\\n [172417, 0, 0, 0, 0, 0, -172417, 0, 0, 0, 0, 0],\\n [0, 1.43681e+06, 0, 0, 0, 862083, 0, -1.43681e+06, 0, 0, 0, 862083],\\n [0, 0, 1.43681e+06, 0, -862083, 0, 0, 0, -1.43681e+06, 0, -862083, 0],\\n [0, 0, 0, 66828.2, 0, 0, 0, 0, 0, -66828.2, 0, 0],\\n [0, 0, -862083, 0, 689667, 0, 0, 0, 862083, 0, 344833, 0],\\n [0, 862083, 0, 0, 0, 689667, 0, -862083, 0, 0, 0, 344833],\\n [-172417, 0, 0, 0, 0, 0, 172417, 0, 0, 0, 0, 0],\\n [0, -1.43681e+06, 0, 0, 0, -862083, 0, 1.43681e+06, 0, 0, 0, -862083],\\n [0, 0, -1.43681e+06, 0, 862083, 0, 0, 0, 1.43681e+06, 0, 862083, 0],\\n [0, 0, 0, -66828.2, 0, 0, 0, 0, 0, 66828.2, 0, 0],\\n [0, 0, -862083, 0, 344833, 0, 0, 0, 862083, 0, 689667, 0],\\n [0, 862083, 0, 0, 0, 344833, 0, -862083, 0, 0, 0, 689667]\\n ])\\n\\n Ke_mat = element.Ke_mat\\n\\n try:\\n assert (abs(Ke_mat_kratos - Ke_mat) < 10).all()\\n except AssertionError:\\n msg = \\\"##################################################################################\\\\n\\\"\\n msg += \\\"Material Stiffness matrix\\\\n\\\"\\n msg += \\\"Ke_mat in Kratos:\\\\n\\\" + str(Ke_mat_kratos)\\n msg += \\\"\\\\nIt is however:\\\\n\\\" + str(Ke_mat)\\n print(msg)\\n\\n Phiz = 0.0\\n Phiy = 0.0\\n\\n CTy = (element.rho * element.A * element.L) / ((1 + Phiy) * (1 + Phiy))\\n CTz = (element.rho * element.A * element.L) / ((1 + Phiz) * (1 + Phiz))\\n\\n CRy = (element.rho * element.Iy) / ((1 + Phiy) * (1 + Phiy) * element.L)\\n CRz = (element.rho * element.Iz) / ((1 + Phiz) * (1 + Phiz) * element.L)\\n\\n bending_mass_matrix_z = element.build_single_mass_matrix(Phiz, CTz, CRz, element.L, +1)\\n\\n bending_mass_matrix_kratos_z = np.array([\\n [1.13489, 0.137711, -0.663886, 0.0435114],\\n [0.137711, 0.138519, -0.0435114, -0.0410891],\\n [-0.663886, -0.0435114, 1.13489, -0.137711],\\n [0.0435114, -0.0410891, -0.137711, 0.138519]\\n ])\\n\\n try:\\n assert (abs(bending_mass_matrix_z - bending_mass_matrix_kratos_z) < 1e-4).all()\\n print(\\\"Bending mass_matrix z is correct\\\")\\n except AssertionError:\\n msg = \\\"##################################################################################\\\\n\\\"\\n msg += \\\"Bending mass matrix z\\\\n\\\"\\n msg += \\\"Me in Kratos:\\\\n\\\" + str(bending_mass_matrix_kratos_z)\\n msg += \\\"\\\\nIt is however:\\\\n\\\" + str(bending_mass_matrix_z)\\n print(msg)\\n\\n bending_mass_matrix_y = element.build_single_mass_matrix(Phiz, CTy, CRy, element.L, -1)\\n\\n bending_mass_matrix_kratos_y = np.array([\\n [1.13489, -0.137711, -0.663886, -0.0435114],\\n [-0.137711, 0.138519, 0.0435114, -0.0410891],\\n [-0.663886, 0.0435114, 1.13489, 0.137711],\\n [-0.0435114, -0.0410891, 0.137711, 0.138519]\\n ])\\n\\n try:\\n assert (abs(bending_mass_matrix_y - bending_mass_matrix_kratos_y) < 1e-4).all()\\n print(\\\"Bending mass_matrix y is correct\\\")\\n except AssertionError:\\n msg = \\\"##################################################################################\\\\n\\\"\\n msg += \\\"Bending mass matrix y\\\\n\\\"\\n msg += \\\"Me in Kratos:\\\\n\\\" + str(bending_mass_matrix_kratos_y)\\n msg += \\\"\\\\nIt is however:\\\\n\\\" + str(bending_mass_matrix_y)\\n print(msg)\\n\\n Me = element._get_consistent_mass_matrix()\\n\\n Me_kratos = np.array([\\n [0.314, 0, 0, 0, 0, 0, 0.157, 0, 0, 0, 0, 0],\\n [0, 1.13489, 0, 0, 0, 0.137711, 0, -0.663886, 0, 0, 0, 0.0435114],\\n [0, 0, 1.13489, 0, -0.137711, 0, 0, 0, -0.663886, 0, -0.0435114, 0],\\n [0, 0, 0, 0.628, 0, 0, 0, 0, 0, 0.314, 0, 0],\\n [0, 0, -0.137711, 0, 0.138519, 0, 0, 0, 0.0435114, 0, -0.0410891, 0],\\n [0, 0.137711, 0, 0, 0, 0.138519, 0, -0.0435114, 0, 0, 0, -0.0410891],\\n [0.157, 0, 0, 0, 0, 0, 0.314, 0, 0, 0, 0, 0],\\n [0, -0.663886, 0, 0, 0, -0.0435114, 0, 1.13489, 0, 0, 0, -0.137711],\\n [0, 0, -0.663886, 0, 0.0435114, 0, 0, 0, 1.13489, 0, 0.137711, 0],\\n [0, 0, 0, 0.314, 0, 0, 0, 0, 0, 0.628, 0, 0],\\n [0, 0, -0.0435114, 0, -0.0410891, 0, 0, 0, 0.137711, 0, 0.138519, 0],\\n [0, 0.0435114, 0, 0, 0, -0.0410891, 0, -0.137711, 0, 0, 0, 0.138519]\\n ])\\n\\n try:\\n assert (abs(Me - Me_kratos) < 1e-2).all()\\n print(\\\"Mass matrix is correct\\\")\\n except AssertionError:\\n msg = \\\"##################################################################################\\\\n\\\"\\n msg += \\\"Consistent mass matrix\\\\n\\\"\\n msg += \\\"Me in Kratos:\\\\n\\\" + str(Me_kratos)\\n msg += \\\"\\\\nIt is however:\\\\n\\\" + str(Me)\\n print(msg)\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.set_printoptions","numpy.array"]],"string":"[\n [\n \"numpy.set_printoptions\",\n \"numpy.array\"\n ]\n]","isConversational":false}}},{"rowIdx":1289,"cells":{"repo_name":{"kind":"string","value":"jab0707/UncertainSCI"},"hexsha":{"kind":"list like","value":["569c978c4f67dd7bb37e730276f2a376b8639235"],"string":"[\n \"569c978c4f67dd7bb37e730276f2a376b8639235\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["tests/test_laguerre_inv.py"],"string":"[\n \"tests/test_laguerre_inv.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["import unittest\n\nimport numpy as np\n\nfrom UncertainSCI.families import LaguerrePolynomials\n\n\nclass IDistTestCase(unittest.TestCase):\n \"\"\"\n Tests for (Laguerre polynomial) inversed induced distributions.\n \"\"\"\n\n def test_idistinv_laguerre(self):\n \"\"\"Evaluation of Laguerre inversed induced distribution function.\"\"\"\n\n # Randomly generate x, use idist to generate u\n rho = 11*np.random.random() - 1\n L = LaguerrePolynomials(rho=rho)\n\n n = int(np.ceil(10*np.random.rand(1))[0])\n M = 25\n x1 = 4*(n+1)*np.random.rand(M)\n u = L.idist(x1, n)\n\n # see if idistinv givens x back\n x2 = L.idistinv(u, n)\n\n delta = 5e-3\n ind = np.where(np.abs(x1-x2) > delta)[:2][0]\n if ind.size > 0:\n errstr = 'Failed for rho={0:1.3f}, n={1:d}'.format(rho, n)\n else:\n errstr = ''\n\n self.assertAlmostEqual(np.linalg.norm(x1-x2, ord=np.inf), 0., delta=delta, msg=errstr)\n\n\nif __name__ == \"__main__\":\n\n unittest.main(verbosity=2)\n"],"string":"[\n \"import unittest\\n\\nimport numpy as np\\n\\nfrom UncertainSCI.families import LaguerrePolynomials\\n\\n\\nclass IDistTestCase(unittest.TestCase):\\n \\\"\\\"\\\"\\n Tests for (Laguerre polynomial) inversed induced distributions.\\n \\\"\\\"\\\"\\n\\n def test_idistinv_laguerre(self):\\n \\\"\\\"\\\"Evaluation of Laguerre inversed induced distribution function.\\\"\\\"\\\"\\n\\n # Randomly generate x, use idist to generate u\\n rho = 11*np.random.random() - 1\\n L = LaguerrePolynomials(rho=rho)\\n\\n n = int(np.ceil(10*np.random.rand(1))[0])\\n M = 25\\n x1 = 4*(n+1)*np.random.rand(M)\\n u = L.idist(x1, n)\\n\\n # see if idistinv givens x back\\n x2 = L.idistinv(u, n)\\n\\n delta = 5e-3\\n ind = np.where(np.abs(x1-x2) > delta)[:2][0]\\n if ind.size > 0:\\n errstr = 'Failed for rho={0:1.3f}, n={1:d}'.format(rho, n)\\n else:\\n errstr = ''\\n\\n self.assertAlmostEqual(np.linalg.norm(x1-x2, ord=np.inf), 0., delta=delta, msg=errstr)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n\\n unittest.main(verbosity=2)\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.abs","numpy.random.random","numpy.linalg.norm","numpy.random.rand"]],"string":"[\n [\n \"numpy.abs\",\n \"numpy.random.random\",\n \"numpy.linalg.norm\",\n \"numpy.random.rand\"\n ]\n]","isConversational":false}}},{"rowIdx":1290,"cells":{"repo_name":{"kind":"string","value":"alnah005/aggregation-for-caesar"},"hexsha":{"kind":"list like","value":["d6bca0a1126e0397315d5773401c71075c33ee2f"],"string":"[\n \"d6bca0a1126e0397315d5773401c71075c33ee2f\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["panoptes_aggregation/running_reducers/tess_user_reducer.py"],"string":"[\n \"panoptes_aggregation/running_reducers/tess_user_reducer.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["'''\nTESS User Reducer\n-----------------\nThis module porvides functions to calculate uesr weights for the TESS project.\nExtracts are from Ceasars `PluckFieldExtractor`.\n'''\nfrom .running_reducer_wrapper import running_reducer_wrapper\nimport numpy as np\n\n\n@running_reducer_wrapper(relevant_reduction=True)\ndef tess_user_reducer(data, **kwargs):\n '''Calculate TESS user weights\n\n Parameters\n ----------\n data : list\n A list with one item containing the extract with the user's feedback on a\n gold standard subject\n store : keyword, dict\n A dictinary with two keys:\n\n * `seed`: sum of all previous `seed` values\n * `count`: sum of all previous gold standard transits seen\n relevant_reduction : keyword, list\n A list with one item containing the results of the current subject's stats reducer.\n This item is a dictinary with two keys:\n\n * `True`: number of users who correctly identified the gold standard transits in the subject\n * `False`: number of users who incorrectly identified the gold standard transits in the subject\n\n Returns\n -------\n reduction : dict\n A dictinary with two keys:\n\n * `data`: A dictionary with the `skill` value as the only item\n * `store`: The updated store for the user\n '''\n success = [d['success'] for d in data[0]['feedback']]\n store = kwargs.pop('store')\n relevant_reduction = kwargs.pop('relevant_reduction')[0]\n try:\n d_subject = relevant_reduction['data']['difficulty']\n except:\n d_subject = 0\n\n seed_current = (np.where(success, 2, -1) * d_subject).sum()\n seed = store.get('seed', 0) + seed_current\n count = store.get('count', 0) + len(success)\n store = {\n 'seed': seed,\n 'count': count\n }\n c0 = 1\n skill = c0 * pow((1.0 + np.log10(count)), (seed / count))\n skill = min([3.0, max([0.05, skill])])\n return {\n 'skill': skill,\n '_store': store\n }\n"],"string":"[\n \"'''\\nTESS User Reducer\\n-----------------\\nThis module porvides functions to calculate uesr weights for the TESS project.\\nExtracts are from Ceasars `PluckFieldExtractor`.\\n'''\\nfrom .running_reducer_wrapper import running_reducer_wrapper\\nimport numpy as np\\n\\n\\n@running_reducer_wrapper(relevant_reduction=True)\\ndef tess_user_reducer(data, **kwargs):\\n '''Calculate TESS user weights\\n\\n Parameters\\n ----------\\n data : list\\n A list with one item containing the extract with the user's feedback on a\\n gold standard subject\\n store : keyword, dict\\n A dictinary with two keys:\\n\\n * `seed`: sum of all previous `seed` values\\n * `count`: sum of all previous gold standard transits seen\\n relevant_reduction : keyword, list\\n A list with one item containing the results of the current subject's stats reducer.\\n This item is a dictinary with two keys:\\n\\n * `True`: number of users who correctly identified the gold standard transits in the subject\\n * `False`: number of users who incorrectly identified the gold standard transits in the subject\\n\\n Returns\\n -------\\n reduction : dict\\n A dictinary with two keys:\\n\\n * `data`: A dictionary with the `skill` value as the only item\\n * `store`: The updated store for the user\\n '''\\n success = [d['success'] for d in data[0]['feedback']]\\n store = kwargs.pop('store')\\n relevant_reduction = kwargs.pop('relevant_reduction')[0]\\n try:\\n d_subject = relevant_reduction['data']['difficulty']\\n except:\\n d_subject = 0\\n\\n seed_current = (np.where(success, 2, -1) * d_subject).sum()\\n seed = store.get('seed', 0) + seed_current\\n count = store.get('count', 0) + len(success)\\n store = {\\n 'seed': seed,\\n 'count': count\\n }\\n c0 = 1\\n skill = c0 * pow((1.0 + np.log10(count)), (seed / count))\\n skill = min([3.0, max([0.05, skill])])\\n return {\\n 'skill': skill,\\n '_store': store\\n }\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.log10","numpy.where"]],"string":"[\n [\n \"numpy.log10\",\n \"numpy.where\"\n ]\n]","isConversational":false}}},{"rowIdx":1291,"cells":{"repo_name":{"kind":"string","value":"StarGazer1995/OpenPCDet"},"hexsha":{"kind":"list like","value":["4af33e8badb0c8e68c7c94c71b0ec5667aad2348"],"string":"[\n \"4af33e8badb0c8e68c7c94c71b0ec5667aad2348\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["pcdet/models/backbones_3d/spconv_unet.py"],"string":"[\n \"pcdet/models/backbones_3d/spconv_unet.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["import torch\nimport torch.nn as nn\nimport spconv\nfrom functools import partial\nfrom .spconv_backbone import post_act_block\nfrom ...utils import common_utils\n\n\nclass SparseBasicBlock(spconv.SparseModule):\n expansion = 1\n\n def __init__(self, inplanes, planes, stride=1, downsample=None, indice_key=None, norm_fn=None):\n super(SparseBasicBlock, self).__init__()\n self.conv1 = spconv.SubMConv3d(\n inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False, indice_key=indice_key\n )\n self.bn1 = norm_fn(planes)\n self.relu = nn.ReLU()\n self.conv2 = spconv.SubMConv3d(\n planes, planes, kernel_size=3, stride=1, padding=1, bias=False, indice_key=indice_key\n )\n self.bn2 = norm_fn(planes)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n identity = x.features\n\n assert x.features.dim() == 2, 'x.features.dim()=%d' % x.features.dim()\n\n out = self.conv1(x)\n out.features = self.bn1(out.features)\n out.features = self.relu(out.features)\n\n out = self.conv2(out)\n out.features = self.bn2(out.features)\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out.features += identity\n out.features = self.relu(out.features)\n\n return out\n\n\nclass UNetV2(nn.Module):\n \"\"\"\n Sparse Convolution based UNet for point-wise feature learning.\n Reference Paper: https://arxiv.org/abs/1907.03670 (Shaoshuai Shi, et. al)\n From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network\n \"\"\"\n def __init__(self, model_cfg, input_channels, grid_size, voxel_size, point_cloud_range, **kwargs):\n super().__init__()\n self.model_cfg = model_cfg\n self.sparse_shape = grid_size[::-1] + [1, 0, 0]\n self.voxel_size = voxel_size\n self.point_cloud_range = point_cloud_range\n\n norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)\n\n self.conv_input = spconv.SparseSequential(\n spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),\n norm_fn(16),\n nn.ReLU(),\n )\n block = post_act_block\n\n self.conv1 = spconv.SparseSequential(\n block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),\n )\n\n self.conv2 = spconv.SparseSequential(\n # [1600, 1408, 41] <- [800, 704, 21]\n block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),\n block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),\n block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),\n )\n\n self.conv3 = spconv.SparseSequential(\n # [800, 704, 21] <- [400, 352, 11]\n block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),\n block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),\n block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),\n )\n\n self.conv4 = spconv.SparseSequential(\n # [400, 352, 11] <- [200, 176, 5]\n block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),\n block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),\n block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),\n )\n\n last_pad = 0\n last_pad = self.model_cfg.get('last_pad', last_pad)\n\n self.conv_out = spconv.SparseSequential(\n # [200, 150, 5] -> [200, 150, 2]\n spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,\n bias=False, indice_key='spconv_down2'),\n norm_fn(128),\n nn.ReLU(),\n )\n\n # decoder\n # [400, 352, 11] <- [200, 176, 5]\n self.conv_up_t4 = SparseBasicBlock(64, 64, indice_key='subm4', norm_fn=norm_fn)\n self.conv_up_m4 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4')\n self.inv_conv4 = block(64, 64, 3, norm_fn=norm_fn, indice_key='spconv4', conv_type='inverseconv')\n\n # [800, 704, 21] <- [400, 352, 11]\n self.conv_up_t3 = SparseBasicBlock(64, 64, indice_key='subm3', norm_fn=norm_fn)\n self.conv_up_m3 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3')\n self.inv_conv3 = block(64, 32, 3, norm_fn=norm_fn, indice_key='spconv3', conv_type='inverseconv')\n\n # [1600, 1408, 41] <- [800, 704, 21]\n self.conv_up_t2 = SparseBasicBlock(32, 32, indice_key='subm2', norm_fn=norm_fn)\n self.conv_up_m2 = block(64, 32, 3, norm_fn=norm_fn, indice_key='subm2')\n self.inv_conv2 = block(32, 16, 3, norm_fn=norm_fn, indice_key='spconv2', conv_type='inverseconv')\n\n # [1600, 1408, 41] <- [1600, 1408, 41]\n self.conv_up_t1 = SparseBasicBlock(16, 16, indice_key='subm1', norm_fn=norm_fn)\n self.conv_up_m1 = block(32, 16, 3, norm_fn=norm_fn, indice_key='subm1')\n\n self.conv5 = spconv.SparseSequential(\n block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1')\n )\n self.num_point_features = 16\n\n def UR_block_forward(self, x_lateral, x_bottom, conv_t, conv_m, conv_inv):\n x_trans = conv_t(x_lateral)\n x = x_trans\n x.features = torch.cat((x_bottom.features, x_trans.features), dim=1)\n x_m = conv_m(x)\n x = self.channel_reduction(x, x_m.features.shape[1])\n x.features = x_m.features + x.features\n x = conv_inv(x)\n return x\n\n @staticmethod\n def channel_reduction(x, out_channels):\n \"\"\"\n Args:\n x: x.features (N, C1)\n out_channels: C2\n\n Returns:\n\n \"\"\"\n features = x.features\n n, in_channels = features.shape\n assert (in_channels % out_channels == 0) and (in_channels >= out_channels)\n\n x.features = features.view(n, out_channels, -1).sum(dim=2)\n return x\n\n def forward(self, batch_dict):\n \"\"\"\n Args:\n batch_dict:\n batch_size: int\n vfe_features: (num_voxels, C)\n voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]\n Returns:\n batch_dict:\n encoded_spconv_tensor: sparse tensor\n point_features: (N, C)\n \"\"\"\n voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']\n batch_size = batch_dict['batch_size']\n input_sp_tensor = spconv.SparseConvTensor(\n features=voxel_features,\n indices=voxel_coords.int(),\n spatial_shape=self.sparse_shape,\n batch_size=batch_size\n )\n x = self.conv_input(input_sp_tensor)\n \n x_conv1 = self.conv1(x)\n x_conv2 = self.conv2(x_conv1)\n x_conv3 = self.conv3(x_conv2)\n x_conv4 = self.conv4(x_conv3)\n\n # for detection head\n # [200, 176, 5] -> [200, 176, 2]\n out = self.conv_out(x_conv4)\n\n # for segmentation head\n # [400, 352, 11] <- [200, 176, 5]\n x_up4 = self.UR_block_forward(x_conv4, x_conv4, self.conv_up_t4, self.conv_up_m4, self.inv_conv4)\n # [800, 704, 21] <- [400, 352, 11]\n x_up3 = self.UR_block_forward(x_conv3, x_up4, self.conv_up_t3, self.conv_up_m3, self.inv_conv3)\n # [1600, 1408, 41] <- [800, 704, 21]\n x_up2 = self.UR_block_forward(x_conv2, x_up3, self.conv_up_t2, self.conv_up_m2, self.inv_conv2)\n # [1600, 1408, 41] <- [1600, 1408, 41]\n x_up1 = self.UR_block_forward(x_conv1, x_up2, self.conv_up_t1, self.conv_up_m1, self.conv5)\n\n batch_dict['point_features'] = x_up1.features\n point_coords = common_utils.get_voxel_centers(\n x_up1.indices[:, 1:], downsample_times=1, voxel_size=self.voxel_size,\n point_cloud_range=self.point_cloud_range\n )\n batch_dict['point_coords'] = torch.cat((x_up1.indices[:, 0:1].float(), point_coords), dim=1)\n batch_dict['encoded_spconv_tensor'] = out\n batch_dict['encoded_spconv_tensor_stride'] = 8\n return batch_dict\n"],"string":"[\n \"import torch\\nimport torch.nn as nn\\nimport spconv\\nfrom functools import partial\\nfrom .spconv_backbone import post_act_block\\nfrom ...utils import common_utils\\n\\n\\nclass SparseBasicBlock(spconv.SparseModule):\\n expansion = 1\\n\\n def __init__(self, inplanes, planes, stride=1, downsample=None, indice_key=None, norm_fn=None):\\n super(SparseBasicBlock, self).__init__()\\n self.conv1 = spconv.SubMConv3d(\\n inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False, indice_key=indice_key\\n )\\n self.bn1 = norm_fn(planes)\\n self.relu = nn.ReLU()\\n self.conv2 = spconv.SubMConv3d(\\n planes, planes, kernel_size=3, stride=1, padding=1, bias=False, indice_key=indice_key\\n )\\n self.bn2 = norm_fn(planes)\\n self.downsample = downsample\\n self.stride = stride\\n\\n def forward(self, x):\\n identity = x.features\\n\\n assert x.features.dim() == 2, 'x.features.dim()=%d' % x.features.dim()\\n\\n out = self.conv1(x)\\n out.features = self.bn1(out.features)\\n out.features = self.relu(out.features)\\n\\n out = self.conv2(out)\\n out.features = self.bn2(out.features)\\n\\n if self.downsample is not None:\\n identity = self.downsample(x)\\n\\n out.features += identity\\n out.features = self.relu(out.features)\\n\\n return out\\n\\n\\nclass UNetV2(nn.Module):\\n \\\"\\\"\\\"\\n Sparse Convolution based UNet for point-wise feature learning.\\n Reference Paper: https://arxiv.org/abs/1907.03670 (Shaoshuai Shi, et. al)\\n From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network\\n \\\"\\\"\\\"\\n def __init__(self, model_cfg, input_channels, grid_size, voxel_size, point_cloud_range, **kwargs):\\n super().__init__()\\n self.model_cfg = model_cfg\\n self.sparse_shape = grid_size[::-1] + [1, 0, 0]\\n self.voxel_size = voxel_size\\n self.point_cloud_range = point_cloud_range\\n\\n norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)\\n\\n self.conv_input = spconv.SparseSequential(\\n spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),\\n norm_fn(16),\\n nn.ReLU(),\\n )\\n block = post_act_block\\n\\n self.conv1 = spconv.SparseSequential(\\n block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),\\n )\\n\\n self.conv2 = spconv.SparseSequential(\\n # [1600, 1408, 41] <- [800, 704, 21]\\n block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),\\n block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),\\n block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),\\n )\\n\\n self.conv3 = spconv.SparseSequential(\\n # [800, 704, 21] <- [400, 352, 11]\\n block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),\\n block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),\\n block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),\\n )\\n\\n self.conv4 = spconv.SparseSequential(\\n # [400, 352, 11] <- [200, 176, 5]\\n block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),\\n block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),\\n block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),\\n )\\n\\n last_pad = 0\\n last_pad = self.model_cfg.get('last_pad', last_pad)\\n\\n self.conv_out = spconv.SparseSequential(\\n # [200, 150, 5] -> [200, 150, 2]\\n spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,\\n bias=False, indice_key='spconv_down2'),\\n norm_fn(128),\\n nn.ReLU(),\\n )\\n\\n # decoder\\n # [400, 352, 11] <- [200, 176, 5]\\n self.conv_up_t4 = SparseBasicBlock(64, 64, indice_key='subm4', norm_fn=norm_fn)\\n self.conv_up_m4 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4')\\n self.inv_conv4 = block(64, 64, 3, norm_fn=norm_fn, indice_key='spconv4', conv_type='inverseconv')\\n\\n # [800, 704, 21] <- [400, 352, 11]\\n self.conv_up_t3 = SparseBasicBlock(64, 64, indice_key='subm3', norm_fn=norm_fn)\\n self.conv_up_m3 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3')\\n self.inv_conv3 = block(64, 32, 3, norm_fn=norm_fn, indice_key='spconv3', conv_type='inverseconv')\\n\\n # [1600, 1408, 41] <- [800, 704, 21]\\n self.conv_up_t2 = SparseBasicBlock(32, 32, indice_key='subm2', norm_fn=norm_fn)\\n self.conv_up_m2 = block(64, 32, 3, norm_fn=norm_fn, indice_key='subm2')\\n self.inv_conv2 = block(32, 16, 3, norm_fn=norm_fn, indice_key='spconv2', conv_type='inverseconv')\\n\\n # [1600, 1408, 41] <- [1600, 1408, 41]\\n self.conv_up_t1 = SparseBasicBlock(16, 16, indice_key='subm1', norm_fn=norm_fn)\\n self.conv_up_m1 = block(32, 16, 3, norm_fn=norm_fn, indice_key='subm1')\\n\\n self.conv5 = spconv.SparseSequential(\\n block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1')\\n )\\n self.num_point_features = 16\\n\\n def UR_block_forward(self, x_lateral, x_bottom, conv_t, conv_m, conv_inv):\\n x_trans = conv_t(x_lateral)\\n x = x_trans\\n x.features = torch.cat((x_bottom.features, x_trans.features), dim=1)\\n x_m = conv_m(x)\\n x = self.channel_reduction(x, x_m.features.shape[1])\\n x.features = x_m.features + x.features\\n x = conv_inv(x)\\n return x\\n\\n @staticmethod\\n def channel_reduction(x, out_channels):\\n \\\"\\\"\\\"\\n Args:\\n x: x.features (N, C1)\\n out_channels: C2\\n\\n Returns:\\n\\n \\\"\\\"\\\"\\n features = x.features\\n n, in_channels = features.shape\\n assert (in_channels % out_channels == 0) and (in_channels >= out_channels)\\n\\n x.features = features.view(n, out_channels, -1).sum(dim=2)\\n return x\\n\\n def forward(self, batch_dict):\\n \\\"\\\"\\\"\\n Args:\\n batch_dict:\\n batch_size: int\\n vfe_features: (num_voxels, C)\\n voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]\\n Returns:\\n batch_dict:\\n encoded_spconv_tensor: sparse tensor\\n point_features: (N, C)\\n \\\"\\\"\\\"\\n voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']\\n batch_size = batch_dict['batch_size']\\n input_sp_tensor = spconv.SparseConvTensor(\\n features=voxel_features,\\n indices=voxel_coords.int(),\\n spatial_shape=self.sparse_shape,\\n batch_size=batch_size\\n )\\n x = self.conv_input(input_sp_tensor)\\n \\n x_conv1 = self.conv1(x)\\n x_conv2 = self.conv2(x_conv1)\\n x_conv3 = self.conv3(x_conv2)\\n x_conv4 = self.conv4(x_conv3)\\n\\n # for detection head\\n # [200, 176, 5] -> [200, 176, 2]\\n out = self.conv_out(x_conv4)\\n\\n # for segmentation head\\n # [400, 352, 11] <- [200, 176, 5]\\n x_up4 = self.UR_block_forward(x_conv4, x_conv4, self.conv_up_t4, self.conv_up_m4, self.inv_conv4)\\n # [800, 704, 21] <- [400, 352, 11]\\n x_up3 = self.UR_block_forward(x_conv3, x_up4, self.conv_up_t3, self.conv_up_m3, self.inv_conv3)\\n # [1600, 1408, 41] <- [800, 704, 21]\\n x_up2 = self.UR_block_forward(x_conv2, x_up3, self.conv_up_t2, self.conv_up_m2, self.inv_conv2)\\n # [1600, 1408, 41] <- [1600, 1408, 41]\\n x_up1 = self.UR_block_forward(x_conv1, x_up2, self.conv_up_t1, self.conv_up_m1, self.conv5)\\n\\n batch_dict['point_features'] = x_up1.features\\n point_coords = common_utils.get_voxel_centers(\\n x_up1.indices[:, 1:], downsample_times=1, voxel_size=self.voxel_size,\\n point_cloud_range=self.point_cloud_range\\n )\\n batch_dict['point_coords'] = torch.cat((x_up1.indices[:, 0:1].float(), point_coords), dim=1)\\n batch_dict['encoded_spconv_tensor'] = out\\n batch_dict['encoded_spconv_tensor_stride'] = 8\\n return batch_dict\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["torch.nn.ReLU","torch.cat"]],"string":"[\n [\n \"torch.nn.ReLU\",\n \"torch.cat\"\n ]\n]","isConversational":false}}},{"rowIdx":1292,"cells":{"repo_name":{"kind":"string","value":"ChenLi0830/Clevo-Categorization-Service"},"hexsha":{"kind":"list like","value":["44b509786849a6dce610171d86e5da68ad748b4b"],"string":"[\n \"44b509786849a6dce610171d86e5da68ad748b4b\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["temp/train_cnn.py"],"string":"[\n \"temp/train_cnn.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["'''This example demonstrates the use of Convolution1D for text classification.\n'''\n\nfrom __future__ import print_function\n\n\nimport sys\nsys.path.append('/Users/wangwei/anaconda2/envs/python3_keras/lib/python3.6/site-packages')\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.layers import Embedding\nfrom keras.layers import Conv1D, GlobalMaxPooling1D\nfrom keras import backend as K\n#os.chdir('/Users/wangwei/cuda_keras_projets/keras/examples/')\n\nimport six.moves.cPickle as pickle # for python 3\n#import cPickle for python 2.7\n\nimport pandas as pd\nimport numpy as np\n\nimport jieba\n\n\n# set parameters:\n\nmaxlen = 64 #11\nbatch_size = 5\nembedding_dims = 300\nfilters = 50 # 100\nkernel_size = 3\nhidden_dims = 100\nepochs = 10\n\ndef get_idx_from_sent(sent, word_idx_map, k=300):\n \"\"\"\n Transforms sentence into a list of indices. \n \"\"\"\n x = []\n words = list(jieba.cut(sent, cut_all=False)) \n\n \n for word in words:\n \n if word in word_idx_map:\n x.append(word_idx_map[word])\n return x\n\ndef make_idx_data_cv(revs, word_idx_map, cv, k=300):\n \"\"\"\n Transforms sentences into a 2-d matrix.\n \"\"\"\n train, test = [], []\n train_y, test_y = [],[]\n for rev in revs:\n sent = get_idx_from_sent(rev['text'], word_idx_map, k)\n \n if rev[\"split\"]==cv:\n test.append(sent)\n test_y.append(rev[\"y\"])\n else:\n train.append(sent)\n train_y.append(rev[\"y\"])\n #train = np.array(train, dtype='int')\n #test = np.array(test, dtype='int')\n \n return [train, test, train_y, test_y]\n\n\n\nif __name__==\"__main__\": \n print('The script that is running is :', __file__)\n print('Depending on the training datasets: \\n maximum length of a sentence is :', maxlen)\n\n\t######### Main code starts here ###########\n print(\"loading data...\")\n x = pickle.load(open(\"mr_folder/mr.p\",\"rb\"), encoding='latin1')\n revs, W, W2, word_idx_map, word_idx_map2, vocab = x[0], x[1], x[2], x[3], x[4],x[5]\n print(\"data loaded!\")\n print(\"using: word2vec vectors\")\n\n tmp = pd.DataFrame(revs)\n\n max_l = np.max(tmp[\"num_words\"])\n print(\"number of sentences: \" , str(len(revs)))\n print(\"vocab size: \" , str(len(vocab)))\n print(\"max sentence length: \" + str(max_l))\n\n max_features = len(vocab)#50\n\n #### Make datasets\n datasets = make_idx_data_cv(revs, word_idx_map2, 1, k=300)\n x_train = datasets[0]\n x_test = datasets[1]\n y_train = datasets[2]\n y_test = datasets[3]\n \n\n print('Pad sequences (samples x time)')\n x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\n x_test = sequence.pad_sequences(x_test, maxlen=maxlen)\n print('x_train shape:', x_train.shape)\n print('x_test shape:', x_test.shape)\n\n ############# modelling with CNN\n import keras\n num_classes = 9\n # convert class vectors to binary class matrices\n y_train = keras.utils.to_categorical(y_train, num_classes)\n y_test = keras.utils.to_categorical(y_test, num_classes)\n print('lengh of y_train is :', y_train.shape[0])\n print('Build model...')\n\n K.clear_session()\n \n \n model = Sequential()\n \n # we start off with an efficient embedding layer which maps\n # our vocab indices into embedding_dims dimensions\n model.add(Embedding(max_features+1,\n\t embedding_dims,\n\t weights=[W],\n\t input_length=maxlen,\n\t trainable=False))\n model.add(Dropout(0.2))\n\n # we add a Convolution1D, which will learn filters\n # word group filters of size filter_length:\n model.add(Conv1D(filters,\n\t kernel_size,\n\t padding='valid',\n\t activation='relu',\n\t strides=1))\n # we use max pooling:\n model.add(GlobalMaxPooling1D())\n\n # We add a vanilla hidden layer:\n model.add(Dense(hidden_dims))\n model.add(Dropout(0.2))\n #model.add(Activation('relu'))\n\n # We project onto a single unit output layer, and squash it with a sigmoid:\n #model.add(Dense(1))\n model.add(Activation('sigmoid'))\n\n\n ######################\n model.add(Dropout(0.2))\n model.add(Dense(num_classes, activation='softmax'))\n # model.compile(loss=keras.losses.categorical_crossentropy,\n # optimizer=keras.optimizers.Adadelta(),\n # metrics=['accuracy'])\n model.compile(optimizer='rmsprop', \n\t loss='categorical_crossentropy', \n\t metrics=['accuracy'])\n model.fit(x_train, y_train,\n\t batch_size=batch_size,\n\t epochs=epochs,\n\t verbose=1,\n\t validation_data=(x_test, y_test))\n score = model.evaluate(x_test, y_test, verbose=0)\n print('Test loss:', score[0])\n print('Test accuracy:', score[1])\n\n # serialize model to JSON\n model_json = model.to_json()\n with open(\"mr_folder/model.json\", \"w\") as json_file:\n json_file.write(model_json)\n # serialize weights to HDF5\n model.save_weights(\"mr_folder/model.h5\")\n print(\"Saved model to disk\")"],"string":"[\n \"'''This example demonstrates the use of Convolution1D for text classification.\\n'''\\n\\nfrom __future__ import print_function\\n\\n\\nimport sys\\nsys.path.append('/Users/wangwei/anaconda2/envs/python3_keras/lib/python3.6/site-packages')\\n\\nfrom keras.preprocessing import sequence\\nfrom keras.models import Sequential\\nfrom keras.layers import Dense, Dropout, Activation\\nfrom keras.layers import Embedding\\nfrom keras.layers import Conv1D, GlobalMaxPooling1D\\nfrom keras import backend as K\\n#os.chdir('/Users/wangwei/cuda_keras_projets/keras/examples/')\\n\\nimport six.moves.cPickle as pickle # for python 3\\n#import cPickle for python 2.7\\n\\nimport pandas as pd\\nimport numpy as np\\n\\nimport jieba\\n\\n\\n# set parameters:\\n\\nmaxlen = 64 #11\\nbatch_size = 5\\nembedding_dims = 300\\nfilters = 50 # 100\\nkernel_size = 3\\nhidden_dims = 100\\nepochs = 10\\n\\ndef get_idx_from_sent(sent, word_idx_map, k=300):\\n \\\"\\\"\\\"\\n Transforms sentence into a list of indices. \\n \\\"\\\"\\\"\\n x = []\\n words = list(jieba.cut(sent, cut_all=False)) \\n\\n \\n for word in words:\\n \\n if word in word_idx_map:\\n x.append(word_idx_map[word])\\n return x\\n\\ndef make_idx_data_cv(revs, word_idx_map, cv, k=300):\\n \\\"\\\"\\\"\\n Transforms sentences into a 2-d matrix.\\n \\\"\\\"\\\"\\n train, test = [], []\\n train_y, test_y = [],[]\\n for rev in revs:\\n sent = get_idx_from_sent(rev['text'], word_idx_map, k)\\n \\n if rev[\\\"split\\\"]==cv:\\n test.append(sent)\\n test_y.append(rev[\\\"y\\\"])\\n else:\\n train.append(sent)\\n train_y.append(rev[\\\"y\\\"])\\n #train = np.array(train, dtype='int')\\n #test = np.array(test, dtype='int')\\n \\n return [train, test, train_y, test_y]\\n\\n\\n\\nif __name__==\\\"__main__\\\": \\n print('The script that is running is :', __file__)\\n print('Depending on the training datasets: \\\\n maximum length of a sentence is :', maxlen)\\n\\n\\t######### Main code starts here ###########\\n print(\\\"loading data...\\\")\\n x = pickle.load(open(\\\"mr_folder/mr.p\\\",\\\"rb\\\"), encoding='latin1')\\n revs, W, W2, word_idx_map, word_idx_map2, vocab = x[0], x[1], x[2], x[3], x[4],x[5]\\n print(\\\"data loaded!\\\")\\n print(\\\"using: word2vec vectors\\\")\\n\\n tmp = pd.DataFrame(revs)\\n\\n max_l = np.max(tmp[\\\"num_words\\\"])\\n print(\\\"number of sentences: \\\" , str(len(revs)))\\n print(\\\"vocab size: \\\" , str(len(vocab)))\\n print(\\\"max sentence length: \\\" + str(max_l))\\n\\n max_features = len(vocab)#50\\n\\n #### Make datasets\\n datasets = make_idx_data_cv(revs, word_idx_map2, 1, k=300)\\n x_train = datasets[0]\\n x_test = datasets[1]\\n y_train = datasets[2]\\n y_test = datasets[3]\\n \\n\\n print('Pad sequences (samples x time)')\\n x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\\n x_test = sequence.pad_sequences(x_test, maxlen=maxlen)\\n print('x_train shape:', x_train.shape)\\n print('x_test shape:', x_test.shape)\\n\\n ############# modelling with CNN\\n import keras\\n num_classes = 9\\n # convert class vectors to binary class matrices\\n y_train = keras.utils.to_categorical(y_train, num_classes)\\n y_test = keras.utils.to_categorical(y_test, num_classes)\\n print('lengh of y_train is :', y_train.shape[0])\\n print('Build model...')\\n\\n K.clear_session()\\n \\n \\n model = Sequential()\\n \\n # we start off with an efficient embedding layer which maps\\n # our vocab indices into embedding_dims dimensions\\n model.add(Embedding(max_features+1,\\n\\t embedding_dims,\\n\\t weights=[W],\\n\\t input_length=maxlen,\\n\\t trainable=False))\\n model.add(Dropout(0.2))\\n\\n # we add a Convolution1D, which will learn filters\\n # word group filters of size filter_length:\\n model.add(Conv1D(filters,\\n\\t kernel_size,\\n\\t padding='valid',\\n\\t activation='relu',\\n\\t strides=1))\\n # we use max pooling:\\n model.add(GlobalMaxPooling1D())\\n\\n # We add a vanilla hidden layer:\\n model.add(Dense(hidden_dims))\\n model.add(Dropout(0.2))\\n #model.add(Activation('relu'))\\n\\n # We project onto a single unit output layer, and squash it with a sigmoid:\\n #model.add(Dense(1))\\n model.add(Activation('sigmoid'))\\n\\n\\n ######################\\n model.add(Dropout(0.2))\\n model.add(Dense(num_classes, activation='softmax'))\\n # model.compile(loss=keras.losses.categorical_crossentropy,\\n # optimizer=keras.optimizers.Adadelta(),\\n # metrics=['accuracy'])\\n model.compile(optimizer='rmsprop', \\n\\t loss='categorical_crossentropy', \\n\\t metrics=['accuracy'])\\n model.fit(x_train, y_train,\\n\\t batch_size=batch_size,\\n\\t epochs=epochs,\\n\\t verbose=1,\\n\\t validation_data=(x_test, y_test))\\n score = model.evaluate(x_test, y_test, verbose=0)\\n print('Test loss:', score[0])\\n print('Test accuracy:', score[1])\\n\\n # serialize model to JSON\\n model_json = model.to_json()\\n with open(\\\"mr_folder/model.json\\\", \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n # serialize weights to HDF5\\n model.save_weights(\\\"mr_folder/model.h5\\\")\\n print(\\\"Saved model to disk\\\")\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.max","pandas.DataFrame"]],"string":"[\n [\n \"numpy.max\",\n \"pandas.DataFrame\"\n ]\n]","isConversational":false}}},{"rowIdx":1293,"cells":{"repo_name":{"kind":"string","value":"MOONJOOYOUNG/AdamP"},"hexsha":{"kind":"list like","value":["64a63106a2ac62bcbe90627f2a83ec1b488f3416"],"string":"[\n \"64a63106a2ac62bcbe90627f2a83ec1b488f3416\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["adamp/sgdp.py"],"string":"[\n \"adamp/sgdp.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["\"\"\"\nAdamP\nCopyright (c) 2020-present NAVER Corp.\nMIT license\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.optim.optimizer import Optimizer, required\nimport math\n\nclass SGDP(Optimizer):\n def __init__(self, params, lr=required, momentum=0, dampening=0,\n weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1):\n defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,\n nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio)\n super(SGDP, self).__init__(params, defaults)\n\n def _channel_view(self, x):\n return x.view(x.size(0), -1)\n\n def _layer_view(self, x):\n return x.view(1, -1)\n\n def _cosine_similarity(self, x, y, eps, view_func):\n x = view_func(x)\n y = view_func(y)\n\n x_norm = x.norm(dim=1).add_(eps)\n y_norm = y.norm(dim=1).add_(eps)\n dot = (x * y).sum(dim=1)\n\n return dot.abs() / x_norm / y_norm\n\n def _projection(self, p, grad, perturb, delta, wd_ratio, eps):\n wd = 1\n expand_size = [-1] + [1] * (len(p.shape) - 1)\n for view_func in [self._channel_view, self._layer_view]:\n\n cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)\n\n if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):\n p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps)\n perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size)\n wd = wd_ratio\n\n return perturb, wd\n\n return perturb, wd\n\n def step(self, closure=None):\n loss = None\n if closure is not None:\n loss = closure()\n\n for group in self.param_groups:\n weight_decay = group['weight_decay']\n momentum = group['momentum']\n dampening = group['dampening']\n nesterov = group['nesterov']\n\n for p in group['params']:\n if p.grad is None:\n continue\n grad = p.grad.data\n state = self.state[p]\n\n # State initialization\n if len(state) == 0:\n state['momentum'] = torch.zeros_like(p.data)\n\n # SGD\n buf = state['momentum']\n buf.mul_(momentum).add_(1 - dampening, grad)\n if nesterov:\n d_p = grad + momentum * buf\n else:\n d_p = buf\n\n # Projection\n wd_ratio = 1\n if len(p.shape) > 1:\n d_p, wd_ratio = self._projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps'])\n\n # Weight decay\n if weight_decay != 0:\n p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum))\n\n # Step\n p.data.add_(-group['lr'], d_p)\n\n return loss\n"],"string":"[\n \"\\\"\\\"\\\"\\nAdamP\\nCopyright (c) 2020-present NAVER Corp.\\nMIT license\\n\\\"\\\"\\\"\\n\\nimport torch\\nimport torch.nn as nn\\nfrom torch.optim.optimizer import Optimizer, required\\nimport math\\n\\nclass SGDP(Optimizer):\\n def __init__(self, params, lr=required, momentum=0, dampening=0,\\n weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1):\\n defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,\\n nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio)\\n super(SGDP, self).__init__(params, defaults)\\n\\n def _channel_view(self, x):\\n return x.view(x.size(0), -1)\\n\\n def _layer_view(self, x):\\n return x.view(1, -1)\\n\\n def _cosine_similarity(self, x, y, eps, view_func):\\n x = view_func(x)\\n y = view_func(y)\\n\\n x_norm = x.norm(dim=1).add_(eps)\\n y_norm = y.norm(dim=1).add_(eps)\\n dot = (x * y).sum(dim=1)\\n\\n return dot.abs() / x_norm / y_norm\\n\\n def _projection(self, p, grad, perturb, delta, wd_ratio, eps):\\n wd = 1\\n expand_size = [-1] + [1] * (len(p.shape) - 1)\\n for view_func in [self._channel_view, self._layer_view]:\\n\\n cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)\\n\\n if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):\\n p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps)\\n perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size)\\n wd = wd_ratio\\n\\n return perturb, wd\\n\\n return perturb, wd\\n\\n def step(self, closure=None):\\n loss = None\\n if closure is not None:\\n loss = closure()\\n\\n for group in self.param_groups:\\n weight_decay = group['weight_decay']\\n momentum = group['momentum']\\n dampening = group['dampening']\\n nesterov = group['nesterov']\\n\\n for p in group['params']:\\n if p.grad is None:\\n continue\\n grad = p.grad.data\\n state = self.state[p]\\n\\n # State initialization\\n if len(state) == 0:\\n state['momentum'] = torch.zeros_like(p.data)\\n\\n # SGD\\n buf = state['momentum']\\n buf.mul_(momentum).add_(1 - dampening, grad)\\n if nesterov:\\n d_p = grad + momentum * buf\\n else:\\n d_p = buf\\n\\n # Projection\\n wd_ratio = 1\\n if len(p.shape) > 1:\\n d_p, wd_ratio = self._projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps'])\\n\\n # Weight decay\\n if weight_decay != 0:\\n p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum))\\n\\n # Step\\n p.data.add_(-group['lr'], d_p)\\n\\n return loss\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["torch.zeros_like"]],"string":"[\n [\n \"torch.zeros_like\"\n ]\n]","isConversational":false}}},{"rowIdx":1294,"cells":{"repo_name":{"kind":"string","value":"antgonza/qtp-diversity"},"hexsha":{"kind":"list like","value":["0c2ec84711decf798ea6ffdb3e97dc9582ba4035"],"string":"[\n \"0c2ec84711decf798ea6ffdb3e97dc9582ba4035\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["qtp_diversity/tests/test_validate.py"],"string":"[\n \"qtp_diversity/tests/test_validate.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["# -----------------------------------------------------------------------------\n# Copyright (c) 2014--, The Qiita Development Team.\n#\n# Distributed under the terms of the BSD 3-clause License.\n#\n# The full license is in the file LICENSE, distributed with this software.\n# -----------------------------------------------------------------------------\n\nfrom unittest import main\nfrom tempfile import mkdtemp, mkstemp\nfrom os.path import exists, isdir, join\nfrom os import remove, close\nfrom shutil import rmtree\nfrom json import dumps\n\nfrom skbio.stats.distance import randdm\nfrom skbio import OrdinationResults\nfrom qiita_client import ArtifactInfo\nfrom qiita_client.testing import PluginTestCase\nimport pandas as pd\nimport numpy as np\n\nfrom qtp_diversity import plugin\nfrom qtp_diversity.validate import (\n _validate_distance_matrix, _validate_ordination_results,\n _validate_alpha_vector, _validate_feature_data_taxonomy, validate)\n\n\nclass ValidateTests(PluginTestCase):\n def setUp(self):\n self.out_dir = mkdtemp()\n self._clean_up_files = [self.out_dir]\n self.metadata = {\n '1.SKM4.640180': {'col': \"doesn't really matters\"},\n '1.SKB8.640193': {'col': \"doesn't really matters\"},\n '1.SKD8.640184': {'col': \"doesn't really matters\"},\n '1.SKM9.640192': {'col': \"doesn't really matters\"},\n '1.SKB7.640196': {'col': \"doesn't really matters\"}}\n\n plugin('https://localhost:8383', 'register', 'ignored')\n\n def tearDown(self):\n for fp in self._clean_up_files:\n if exists(fp):\n if isdir(fp):\n rmtree(fp)\n else:\n remove(fp)\n\n def _create_distance_matrix(self, sample_ids):\n dm = randdm(len(sample_ids), sample_ids)\n fd, fp = mkstemp(suffix='.txt', dir=self.out_dir)\n close(fd)\n dm.write(fp)\n return fp\n\n def _create_ordination_results(self, sample_ids):\n eigvals = [0.51236726, 0.30071909, 0.26791207, 0.20898868]\n proportion_explained = [0.2675738328, 0.157044696, 0.1399118638,\n 0.1091402725]\n axis_labels = ['PC1', 'PC2', 'PC3', 'PC4']\n samples = [[-2.584, 1.739, 3.828, -1.944],\n [-2.710, -1.859, -8.648, 1.180],\n [2.350, 9.625, -3.457, -3.208],\n [2.614, -1.114, 1.476, 2.908],\n [2.850, -1.925, 6.232, 1.381]]\n ord_res = OrdinationResults(\n short_method_name='PCoA',\n long_method_name='Principal Coordinate Analysis',\n eigvals=pd.Series(eigvals, index=axis_labels),\n samples=pd.DataFrame(np.asarray(samples), index=sample_ids,\n columns=axis_labels),\n proportion_explained=pd.Series(proportion_explained,\n index=axis_labels))\n fd, fp = mkstemp(suffix='.txt', dir=self.out_dir)\n close(fd)\n ord_res.write(fp)\n return fp\n\n def _create_alpha_vector(self, sample_ids):\n fd, fp = mkstemp(suffix='.txt', dir=self.out_dir)\n close(fd)\n with open(fp, 'w') as f:\n f.write(\"\\tobserved_otus\\n\")\n for s_id in sample_ids:\n f.write(\"%s\\t%d\\n\" % (s_id, np.random.randint(1, 200)))\n\n return fp\n\n def _create_job(self, a_type, files, analysis):\n parameters = {'template': None,\n 'files': dumps(files),\n 'artifact_type': a_type,\n 'analysis': analysis}\n data = {'command': dumps(['Diversity types', '0.1.1', 'Validate']),\n 'parameters': dumps(parameters),\n 'status': 'running'}\n job_id = self.qclient.post(\n '/apitest/processing_job/', data=data)['job']\n return job_id, parameters\n\n def test_validate_distance_matrix(self):\n # Create a distance matrix\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\n '1.SKM9.640192', '1.SKB7.640196']\n dm_fp = self._create_distance_matrix(sample_ids)\n\n # Test success\n obs_success, obs_ainfo, obs_error = _validate_distance_matrix(\n {'plain_text': [dm_fp]}, self.metadata, self.out_dir)\n self.assertTrue(obs_success)\n exp_ainfo = [ArtifactInfo(None, \"distance_matrix\",\n [(dm_fp, 'plain_text')])]\n self.assertEqual(obs_ainfo, exp_ainfo)\n self.assertEqual(obs_error, \"\")\n\n # Test failure\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\n '1.SKM9.640192', 'NotASample']\n dm_fp = self._create_distance_matrix(sample_ids)\n obs_success, obs_ainfo, obs_error = _validate_distance_matrix(\n {'plain_text': [dm_fp]}, self.metadata, self.out_dir)\n self.assertFalse(obs_success)\n self.assertIsNone(obs_ainfo)\n self.assertEqual(obs_error, \"The distance matrix contain samples not \"\n \"present in the metadata\")\n\n def test_validate_ordination_results(self):\n # Create the ordination results\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\n '1.SKM9.640192', '1.SKB7.640196']\n ord_res_fp = self._create_ordination_results(sample_ids)\n\n # Test success\n obs_success, obs_ainfo, obs_error = _validate_ordination_results(\n {'plain_text': [ord_res_fp]}, self.metadata, self.out_dir)\n self.assertTrue(obs_success)\n exp_ainfo = [ArtifactInfo(None, \"ordination_results\",\n [(ord_res_fp, 'plain_text')])]\n self.assertEqual(obs_ainfo, exp_ainfo)\n self.assertEqual(obs_error, \"\")\n\n # Test failure\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\n '1.SKM9.640192', 'NotASample']\n ord_res_fp = self._create_ordination_results(sample_ids)\n obs_success, obs_ainfo, obs_error = _validate_ordination_results(\n {'plain_text': [ord_res_fp]}, self.metadata, self.out_dir)\n self.assertFalse(obs_success)\n self.assertIsNone(obs_ainfo)\n self.assertEqual(obs_error, \"The ordination results contain samples \"\n \"not present in the metadata\")\n\n def test_validate_alpha_vector(self):\n # Create the alpha vector\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\n '1.SKM9.640192']\n alpha_vector_fp = self._create_alpha_vector(sample_ids)\n\n # Test success\n obs_success, obs_ainfo, obs_error = _validate_alpha_vector(\n {'plain_text': [alpha_vector_fp]}, self.metadata, self.out_dir)\n self.assertEqual(obs_error, \"\")\n self.assertTrue(obs_success)\n exp_ainfo = [ArtifactInfo(None, \"alpha_vector\",\n [(alpha_vector_fp, 'plain_text')])]\n self.assertEqual(obs_ainfo, exp_ainfo)\n\n # Test failure wrong ids\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\n 'NotASample']\n alpha_vector_fp = self._create_alpha_vector(sample_ids)\n obs_success, obs_ainfo, obs_error = _validate_alpha_vector(\n {'plain_text': [alpha_vector_fp]}, self.metadata, self.out_dir)\n self.assertEqual(obs_error, \"The alpha vector contains samples not \"\n \"present in the metadata\")\n self.assertFalse(obs_success)\n self.assertIsNone(obs_ainfo)\n\n # Test failure wrong format\n fd, alpha_vector_fp = mkstemp(suffix='.txt', dir=self.out_dir)\n close(fd)\n with open(alpha_vector_fp, 'w') as f:\n f.write(\"\\tobserved_otus\\nsample 1\\n\")\n obs_success, obs_ainfo, obs_error = _validate_alpha_vector(\n {'plain_text': [alpha_vector_fp]}, self.metadata, self.out_dir)\n self.assertEqual(obs_error, \"The alpha vector format is incorrect\")\n self.assertFalse(obs_success)\n self.assertIsNone(obs_ainfo)\n\n def test_validate(self):\n # Test artifact type error\n job_id, params = self._create_job(\n 'NotAType', {'plan_text': 'Will fail before checking this'}, 1)\n obs_success, obs_ainfo, obs_error = validate(\n self.qclient, job_id, params, self.out_dir)\n self.assertFalse(obs_success)\n self.assertIsNone(obs_ainfo)\n self.assertEqual(\n obs_error, \"Unknown artifact type NotAType. Supported types: \"\n \"FeatureData[Taxonomy], alpha_vector, distance_matrix, \"\n \"ordination_results\")\n\n # Test missing metadata error - to be fair, I don't know how this error\n # can happen in the live system, but better be safe than sorry\n job_id, params = self._create_job(\n 'distance_matrix', {'plan_text': 'Will fail before checking this'},\n None)\n obs_success, obs_ainfo, obs_error = validate(\n self.qclient, job_id, params, self.out_dir)\n self.assertFalse(obs_success)\n self.assertIsNone(obs_ainfo)\n self.assertEqual(\n obs_error, \"Missing metadata information\")\n\n # Test distance matrix success\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\n '1.SKM9.640192', '1.SKB7.640196']\n dm_fp = self._create_distance_matrix(sample_ids)\n job_id, params = self._create_job(\n 'distance_matrix', {'plain_text': [dm_fp]}, 1)\n obs_success, obs_ainfo, obs_error = validate(\n self.qclient, job_id, params, self.out_dir)\n self.assertTrue(obs_success)\n html_fp = join(self.out_dir, 'index.html')\n exp_ainfo = [ArtifactInfo(None, \"distance_matrix\",\n [(dm_fp, 'plain_text'),\n (html_fp, 'html_summary')])]\n self.assertEqual(obs_ainfo, exp_ainfo)\n self.assertEqual(obs_error, \"\")\n\n # Test ordination results success\n ord_res_fp = self._create_ordination_results(sample_ids)\n job_id, params = self._create_job(\n 'ordination_results', {'plain_text': [ord_res_fp]}, 1)\n obs_success, obs_ainfo, obs_error = validate(\n self.qclient, job_id, params, self.out_dir)\n self.assertTrue(obs_success)\n html_fp = join(self.out_dir, 'index.html')\n esf_fp = join(self.out_dir, 'emperor_support_files')\n exp_ainfo = [ArtifactInfo(None, \"ordination_results\",\n [(ord_res_fp, 'plain_text'),\n (html_fp, 'html_summary'),\n (esf_fp, 'html_summary_dir')])]\n self.assertEqual(obs_ainfo, exp_ainfo)\n self.assertEqual(obs_error, \"\")\n\n # Test alpha vector success\n alpha_vector_fp = self._create_alpha_vector(sample_ids)\n job_id, params = self._create_job(\n 'alpha_vector', {'plain_text': [alpha_vector_fp]}, 1)\n obs_success, obs_ainfo, obs_error = validate(\n self.qclient, job_id, params, self.out_dir)\n self.assertTrue(obs_success)\n html_fp = join(self.out_dir, 'index.html')\n sf_fp = join(self.out_dir, 'support_files')\n exp_ainfo = [ArtifactInfo(None, \"alpha_vector\",\n [(alpha_vector_fp, 'plain_text'),\n (html_fp, 'html_summary'),\n (sf_fp, 'html_summary_dir')])]\n self.assertEqual(obs_ainfo, exp_ainfo)\n self.assertEqual(obs_error, \"\")\n\n def test_validate_FeatureData_Taxonomy(self):\n # Create the feature data\n fd, taxonomy_fp = mkstemp(suffix='.txt', dir=self.out_dir)\n close(fd)\n with open(taxonomy_fp, 'w') as f:\n f.write(\"Feature ID\\tTaxonomy\\tConfidence\\n\")\n f.write(\"TACGGAGGA\\tk__Bacteria;p__Bacteroidetes;c__Bacteroidia\\t\"\n \"0.9998743\\n\")\n f.write(\"TACGTAGGG\\tk__Bacteria;p__Firmicutes;c__Clostridia\\t\"\n \"0.9999999\\n\")\n\n # Test success\n obs_success, obs_ainfo, obs_error = _validate_feature_data_taxonomy(\n {'plain_text': [taxonomy_fp]}, None, self.out_dir)\n self.assertEqual(obs_error, \"\")\n self.assertTrue(obs_success)\n exp_ainfo = [ArtifactInfo(None, \"FeatureData[Taxonomy]\",\n [(taxonomy_fp, 'plain_text')])]\n self.assertEqual(obs_ainfo, exp_ainfo)\n\n # Test failure wrong format\n fd, taxonomy_fp = mkstemp(suffix='.txt', dir=self.out_dir)\n close(fd)\n with open(taxonomy_fp, 'w') as f:\n f.write(\"Feature ID\\tIt's gonna fail!\\tConfidence\\n\")\n f.write(\"TACGGAGGA\\tk__Bacteria;p__Bacteroidetes;c__Bacteroidia\\t\"\n \"0.9998743\\n\")\n f.write(\"TACGTAGGG\\tk__Bacteria;p__Firmicutes;c__Clostridia\\t\"\n \"0.9999999\\n\")\n obs_success, obs_ainfo, obs_error = _validate_feature_data_taxonomy(\n {'plain_text': [taxonomy_fp]}, None, self.out_dir)\n self.assertIn(\"The file header seems wrong\", obs_error)\n self.assertFalse(obs_success)\n self.assertIsNone(obs_ainfo)\n\n\nif __name__ == '__main__':\n main()\n"],"string":"[\n \"# -----------------------------------------------------------------------------\\n# Copyright (c) 2014--, The Qiita Development Team.\\n#\\n# Distributed under the terms of the BSD 3-clause License.\\n#\\n# The full license is in the file LICENSE, distributed with this software.\\n# -----------------------------------------------------------------------------\\n\\nfrom unittest import main\\nfrom tempfile import mkdtemp, mkstemp\\nfrom os.path import exists, isdir, join\\nfrom os import remove, close\\nfrom shutil import rmtree\\nfrom json import dumps\\n\\nfrom skbio.stats.distance import randdm\\nfrom skbio import OrdinationResults\\nfrom qiita_client import ArtifactInfo\\nfrom qiita_client.testing import PluginTestCase\\nimport pandas as pd\\nimport numpy as np\\n\\nfrom qtp_diversity import plugin\\nfrom qtp_diversity.validate import (\\n _validate_distance_matrix, _validate_ordination_results,\\n _validate_alpha_vector, _validate_feature_data_taxonomy, validate)\\n\\n\\nclass ValidateTests(PluginTestCase):\\n def setUp(self):\\n self.out_dir = mkdtemp()\\n self._clean_up_files = [self.out_dir]\\n self.metadata = {\\n '1.SKM4.640180': {'col': \\\"doesn't really matters\\\"},\\n '1.SKB8.640193': {'col': \\\"doesn't really matters\\\"},\\n '1.SKD8.640184': {'col': \\\"doesn't really matters\\\"},\\n '1.SKM9.640192': {'col': \\\"doesn't really matters\\\"},\\n '1.SKB7.640196': {'col': \\\"doesn't really matters\\\"}}\\n\\n plugin('https://localhost:8383', 'register', 'ignored')\\n\\n def tearDown(self):\\n for fp in self._clean_up_files:\\n if exists(fp):\\n if isdir(fp):\\n rmtree(fp)\\n else:\\n remove(fp)\\n\\n def _create_distance_matrix(self, sample_ids):\\n dm = randdm(len(sample_ids), sample_ids)\\n fd, fp = mkstemp(suffix='.txt', dir=self.out_dir)\\n close(fd)\\n dm.write(fp)\\n return fp\\n\\n def _create_ordination_results(self, sample_ids):\\n eigvals = [0.51236726, 0.30071909, 0.26791207, 0.20898868]\\n proportion_explained = [0.2675738328, 0.157044696, 0.1399118638,\\n 0.1091402725]\\n axis_labels = ['PC1', 'PC2', 'PC3', 'PC4']\\n samples = [[-2.584, 1.739, 3.828, -1.944],\\n [-2.710, -1.859, -8.648, 1.180],\\n [2.350, 9.625, -3.457, -3.208],\\n [2.614, -1.114, 1.476, 2.908],\\n [2.850, -1.925, 6.232, 1.381]]\\n ord_res = OrdinationResults(\\n short_method_name='PCoA',\\n long_method_name='Principal Coordinate Analysis',\\n eigvals=pd.Series(eigvals, index=axis_labels),\\n samples=pd.DataFrame(np.asarray(samples), index=sample_ids,\\n columns=axis_labels),\\n proportion_explained=pd.Series(proportion_explained,\\n index=axis_labels))\\n fd, fp = mkstemp(suffix='.txt', dir=self.out_dir)\\n close(fd)\\n ord_res.write(fp)\\n return fp\\n\\n def _create_alpha_vector(self, sample_ids):\\n fd, fp = mkstemp(suffix='.txt', dir=self.out_dir)\\n close(fd)\\n with open(fp, 'w') as f:\\n f.write(\\\"\\\\tobserved_otus\\\\n\\\")\\n for s_id in sample_ids:\\n f.write(\\\"%s\\\\t%d\\\\n\\\" % (s_id, np.random.randint(1, 200)))\\n\\n return fp\\n\\n def _create_job(self, a_type, files, analysis):\\n parameters = {'template': None,\\n 'files': dumps(files),\\n 'artifact_type': a_type,\\n 'analysis': analysis}\\n data = {'command': dumps(['Diversity types', '0.1.1', 'Validate']),\\n 'parameters': dumps(parameters),\\n 'status': 'running'}\\n job_id = self.qclient.post(\\n '/apitest/processing_job/', data=data)['job']\\n return job_id, parameters\\n\\n def test_validate_distance_matrix(self):\\n # Create a distance matrix\\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\\n '1.SKM9.640192', '1.SKB7.640196']\\n dm_fp = self._create_distance_matrix(sample_ids)\\n\\n # Test success\\n obs_success, obs_ainfo, obs_error = _validate_distance_matrix(\\n {'plain_text': [dm_fp]}, self.metadata, self.out_dir)\\n self.assertTrue(obs_success)\\n exp_ainfo = [ArtifactInfo(None, \\\"distance_matrix\\\",\\n [(dm_fp, 'plain_text')])]\\n self.assertEqual(obs_ainfo, exp_ainfo)\\n self.assertEqual(obs_error, \\\"\\\")\\n\\n # Test failure\\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\\n '1.SKM9.640192', 'NotASample']\\n dm_fp = self._create_distance_matrix(sample_ids)\\n obs_success, obs_ainfo, obs_error = _validate_distance_matrix(\\n {'plain_text': [dm_fp]}, self.metadata, self.out_dir)\\n self.assertFalse(obs_success)\\n self.assertIsNone(obs_ainfo)\\n self.assertEqual(obs_error, \\\"The distance matrix contain samples not \\\"\\n \\\"present in the metadata\\\")\\n\\n def test_validate_ordination_results(self):\\n # Create the ordination results\\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\\n '1.SKM9.640192', '1.SKB7.640196']\\n ord_res_fp = self._create_ordination_results(sample_ids)\\n\\n # Test success\\n obs_success, obs_ainfo, obs_error = _validate_ordination_results(\\n {'plain_text': [ord_res_fp]}, self.metadata, self.out_dir)\\n self.assertTrue(obs_success)\\n exp_ainfo = [ArtifactInfo(None, \\\"ordination_results\\\",\\n [(ord_res_fp, 'plain_text')])]\\n self.assertEqual(obs_ainfo, exp_ainfo)\\n self.assertEqual(obs_error, \\\"\\\")\\n\\n # Test failure\\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\\n '1.SKM9.640192', 'NotASample']\\n ord_res_fp = self._create_ordination_results(sample_ids)\\n obs_success, obs_ainfo, obs_error = _validate_ordination_results(\\n {'plain_text': [ord_res_fp]}, self.metadata, self.out_dir)\\n self.assertFalse(obs_success)\\n self.assertIsNone(obs_ainfo)\\n self.assertEqual(obs_error, \\\"The ordination results contain samples \\\"\\n \\\"not present in the metadata\\\")\\n\\n def test_validate_alpha_vector(self):\\n # Create the alpha vector\\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\\n '1.SKM9.640192']\\n alpha_vector_fp = self._create_alpha_vector(sample_ids)\\n\\n # Test success\\n obs_success, obs_ainfo, obs_error = _validate_alpha_vector(\\n {'plain_text': [alpha_vector_fp]}, self.metadata, self.out_dir)\\n self.assertEqual(obs_error, \\\"\\\")\\n self.assertTrue(obs_success)\\n exp_ainfo = [ArtifactInfo(None, \\\"alpha_vector\\\",\\n [(alpha_vector_fp, 'plain_text')])]\\n self.assertEqual(obs_ainfo, exp_ainfo)\\n\\n # Test failure wrong ids\\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\\n 'NotASample']\\n alpha_vector_fp = self._create_alpha_vector(sample_ids)\\n obs_success, obs_ainfo, obs_error = _validate_alpha_vector(\\n {'plain_text': [alpha_vector_fp]}, self.metadata, self.out_dir)\\n self.assertEqual(obs_error, \\\"The alpha vector contains samples not \\\"\\n \\\"present in the metadata\\\")\\n self.assertFalse(obs_success)\\n self.assertIsNone(obs_ainfo)\\n\\n # Test failure wrong format\\n fd, alpha_vector_fp = mkstemp(suffix='.txt', dir=self.out_dir)\\n close(fd)\\n with open(alpha_vector_fp, 'w') as f:\\n f.write(\\\"\\\\tobserved_otus\\\\nsample 1\\\\n\\\")\\n obs_success, obs_ainfo, obs_error = _validate_alpha_vector(\\n {'plain_text': [alpha_vector_fp]}, self.metadata, self.out_dir)\\n self.assertEqual(obs_error, \\\"The alpha vector format is incorrect\\\")\\n self.assertFalse(obs_success)\\n self.assertIsNone(obs_ainfo)\\n\\n def test_validate(self):\\n # Test artifact type error\\n job_id, params = self._create_job(\\n 'NotAType', {'plan_text': 'Will fail before checking this'}, 1)\\n obs_success, obs_ainfo, obs_error = validate(\\n self.qclient, job_id, params, self.out_dir)\\n self.assertFalse(obs_success)\\n self.assertIsNone(obs_ainfo)\\n self.assertEqual(\\n obs_error, \\\"Unknown artifact type NotAType. Supported types: \\\"\\n \\\"FeatureData[Taxonomy], alpha_vector, distance_matrix, \\\"\\n \\\"ordination_results\\\")\\n\\n # Test missing metadata error - to be fair, I don't know how this error\\n # can happen in the live system, but better be safe than sorry\\n job_id, params = self._create_job(\\n 'distance_matrix', {'plan_text': 'Will fail before checking this'},\\n None)\\n obs_success, obs_ainfo, obs_error = validate(\\n self.qclient, job_id, params, self.out_dir)\\n self.assertFalse(obs_success)\\n self.assertIsNone(obs_ainfo)\\n self.assertEqual(\\n obs_error, \\\"Missing metadata information\\\")\\n\\n # Test distance matrix success\\n sample_ids = ['1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184',\\n '1.SKM9.640192', '1.SKB7.640196']\\n dm_fp = self._create_distance_matrix(sample_ids)\\n job_id, params = self._create_job(\\n 'distance_matrix', {'plain_text': [dm_fp]}, 1)\\n obs_success, obs_ainfo, obs_error = validate(\\n self.qclient, job_id, params, self.out_dir)\\n self.assertTrue(obs_success)\\n html_fp = join(self.out_dir, 'index.html')\\n exp_ainfo = [ArtifactInfo(None, \\\"distance_matrix\\\",\\n [(dm_fp, 'plain_text'),\\n (html_fp, 'html_summary')])]\\n self.assertEqual(obs_ainfo, exp_ainfo)\\n self.assertEqual(obs_error, \\\"\\\")\\n\\n # Test ordination results success\\n ord_res_fp = self._create_ordination_results(sample_ids)\\n job_id, params = self._create_job(\\n 'ordination_results', {'plain_text': [ord_res_fp]}, 1)\\n obs_success, obs_ainfo, obs_error = validate(\\n self.qclient, job_id, params, self.out_dir)\\n self.assertTrue(obs_success)\\n html_fp = join(self.out_dir, 'index.html')\\n esf_fp = join(self.out_dir, 'emperor_support_files')\\n exp_ainfo = [ArtifactInfo(None, \\\"ordination_results\\\",\\n [(ord_res_fp, 'plain_text'),\\n (html_fp, 'html_summary'),\\n (esf_fp, 'html_summary_dir')])]\\n self.assertEqual(obs_ainfo, exp_ainfo)\\n self.assertEqual(obs_error, \\\"\\\")\\n\\n # Test alpha vector success\\n alpha_vector_fp = self._create_alpha_vector(sample_ids)\\n job_id, params = self._create_job(\\n 'alpha_vector', {'plain_text': [alpha_vector_fp]}, 1)\\n obs_success, obs_ainfo, obs_error = validate(\\n self.qclient, job_id, params, self.out_dir)\\n self.assertTrue(obs_success)\\n html_fp = join(self.out_dir, 'index.html')\\n sf_fp = join(self.out_dir, 'support_files')\\n exp_ainfo = [ArtifactInfo(None, \\\"alpha_vector\\\",\\n [(alpha_vector_fp, 'plain_text'),\\n (html_fp, 'html_summary'),\\n (sf_fp, 'html_summary_dir')])]\\n self.assertEqual(obs_ainfo, exp_ainfo)\\n self.assertEqual(obs_error, \\\"\\\")\\n\\n def test_validate_FeatureData_Taxonomy(self):\\n # Create the feature data\\n fd, taxonomy_fp = mkstemp(suffix='.txt', dir=self.out_dir)\\n close(fd)\\n with open(taxonomy_fp, 'w') as f:\\n f.write(\\\"Feature ID\\\\tTaxonomy\\\\tConfidence\\\\n\\\")\\n f.write(\\\"TACGGAGGA\\\\tk__Bacteria;p__Bacteroidetes;c__Bacteroidia\\\\t\\\"\\n \\\"0.9998743\\\\n\\\")\\n f.write(\\\"TACGTAGGG\\\\tk__Bacteria;p__Firmicutes;c__Clostridia\\\\t\\\"\\n \\\"0.9999999\\\\n\\\")\\n\\n # Test success\\n obs_success, obs_ainfo, obs_error = _validate_feature_data_taxonomy(\\n {'plain_text': [taxonomy_fp]}, None, self.out_dir)\\n self.assertEqual(obs_error, \\\"\\\")\\n self.assertTrue(obs_success)\\n exp_ainfo = [ArtifactInfo(None, \\\"FeatureData[Taxonomy]\\\",\\n [(taxonomy_fp, 'plain_text')])]\\n self.assertEqual(obs_ainfo, exp_ainfo)\\n\\n # Test failure wrong format\\n fd, taxonomy_fp = mkstemp(suffix='.txt', dir=self.out_dir)\\n close(fd)\\n with open(taxonomy_fp, 'w') as f:\\n f.write(\\\"Feature ID\\\\tIt's gonna fail!\\\\tConfidence\\\\n\\\")\\n f.write(\\\"TACGGAGGA\\\\tk__Bacteria;p__Bacteroidetes;c__Bacteroidia\\\\t\\\"\\n \\\"0.9998743\\\\n\\\")\\n f.write(\\\"TACGTAGGG\\\\tk__Bacteria;p__Firmicutes;c__Clostridia\\\\t\\\"\\n \\\"0.9999999\\\\n\\\")\\n obs_success, obs_ainfo, obs_error = _validate_feature_data_taxonomy(\\n {'plain_text': [taxonomy_fp]}, None, self.out_dir)\\n self.assertIn(\\\"The file header seems wrong\\\", obs_error)\\n self.assertFalse(obs_success)\\n self.assertIsNone(obs_ainfo)\\n\\n\\nif __name__ == '__main__':\\n main()\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.asarray","pandas.Series","numpy.random.randint"]],"string":"[\n [\n \"numpy.asarray\",\n \"pandas.Series\",\n \"numpy.random.randint\"\n ]\n]","isConversational":false}}},{"rowIdx":1295,"cells":{"repo_name":{"kind":"string","value":"methane/astropy"},"hexsha":{"kind":"list like","value":["1a065d5ce403e226799cfb3d606fda33be0a6c08","1a065d5ce403e226799cfb3d606fda33be0a6c08"],"string":"[\n \"1a065d5ce403e226799cfb3d606fda33be0a6c08\",\n \"1a065d5ce403e226799cfb3d606fda33be0a6c08\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["astropy/coordinates/sky_coordinate.py","astropy/io/fits/tests/test_core.py"],"string":"[\n \"astropy/coordinates/sky_coordinate.py\",\n \"astropy/io/fits/tests/test_core.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["import re\nimport copy\nimport warnings\nimport operator\n\nimport numpy as np\n\nfrom astropy import _erfa as erfa\nfrom astropy.utils.compat.misc import override__dir__\nfrom astropy import units as u\nfrom astropy.constants import c as speed_of_light\nfrom astropy.utils.data_info import MixinInfo\nfrom astropy.utils import ShapedLikeNDArray\nfrom astropy.time import Time\nfrom astropy.utils.exceptions import AstropyUserWarning\n\nfrom .distances import Distance\nfrom .angles import Angle\nfrom .baseframe import (BaseCoordinateFrame, frame_transform_graph,\n GenericFrame)\nfrom .builtin_frames import ICRS, SkyOffsetFrame\nfrom .representation import (SphericalRepresentation,\n UnitSphericalRepresentation, SphericalDifferential)\nfrom .sky_coordinate_parsers import (_get_frame_class, _get_frame_without_data,\n _parse_coordinate_data)\n\n__all__ = ['SkyCoord', 'SkyCoordInfo']\n\n\nclass SkyCoordInfo(MixinInfo):\n \"\"\"\n Container for meta information like name, description, format. This is\n required when the object is used as a mixin column within a table, but can\n be used as a general way to store meta information.\n \"\"\"\n attrs_from_parent = set(['unit']) # Unit is read-only\n _supports_indexing = False\n\n @staticmethod\n def default_format(val):\n repr_data = val.info._repr_data\n formats = ['{0.' + compname + '.value:}' for compname\n in repr_data.components]\n return ','.join(formats).format(repr_data)\n\n @property\n def unit(self):\n repr_data = self._repr_data\n unit = ','.join(str(getattr(repr_data, comp).unit) or 'None'\n for comp in repr_data.components)\n return unit\n\n @property\n def _repr_data(self):\n if self._parent is None:\n return None\n\n sc = self._parent\n if (issubclass(sc.representation_type, SphericalRepresentation)\n and isinstance(sc.data, UnitSphericalRepresentation)):\n repr_data = sc.represent_as(sc.data.__class__, in_frame_units=True)\n else:\n repr_data = sc.represent_as(sc.representation_type,\n in_frame_units=True)\n return repr_data\n\n def _represent_as_dict(self):\n obj = self._parent\n attrs = (list(obj.representation_component_names) +\n list(frame_transform_graph.frame_attributes.keys()))\n\n # Don't output distance if it is all unitless 1.0\n if 'distance' in attrs and np.all(obj.distance == 1.0):\n attrs.remove('distance')\n\n out = super()._represent_as_dict(attrs)\n\n out['representation_type'] = obj.representation_type.get_name()\n out['frame'] = obj.frame.name\n # Note that obj.info.unit is a fake composite unit (e.g. 'deg,deg,None'\n # or None,None,m) and is not stored. The individual attributes have\n # units.\n\n return out\n\n def new_like(self, skycoords, length, metadata_conflicts='warn', name=None):\n \"\"\"\n Return a new SkyCoord instance which is consistent with the input\n SkyCoord objects ``skycoords`` and has ``length`` rows. Being\n \"consistent\" is defined as being able to set an item from one to each of\n the rest without any exception being raised.\n\n This is intended for creating a new SkyCoord instance whose elements can\n be set in-place for table operations like join or vstack. This is used\n when a SkyCoord object is used as a mixin column in an astropy Table.\n\n The data values are not predictable and it is expected that the consumer\n of the object will fill in all values.\n\n Parameters\n ----------\n skycoords : list\n List of input SkyCoord objects\n length : int\n Length of the output skycoord object\n metadata_conflicts : str ('warn'|'error'|'silent')\n How to handle metadata conflicts\n name : str\n Output name (sets output skycoord.info.name)\n\n Returns\n -------\n skycoord : SkyCoord (or subclass)\n Instance of this class consistent with ``skycoords``\n\n \"\"\"\n # Get merged info attributes like shape, dtype, format, description, etc.\n attrs = self.merge_cols_attributes(skycoords, metadata_conflicts, name,\n ('meta', 'description'))\n skycoord0 = skycoords[0]\n\n # Make a new SkyCoord object with the desired length and attributes\n # by using the _apply / __getitem__ machinery to effectively return\n # skycoord0[[0, 0, ..., 0, 0]]. This will have the all the right frame\n # attributes with the right shape.\n indexes = np.zeros(length, dtype=np.int64)\n out = skycoord0[indexes]\n\n # Use __setitem__ machinery to check for consistency of all skycoords\n for skycoord in skycoords[1:]:\n try:\n out[0] = skycoord[0]\n except Exception as err:\n raise ValueError(f'input skycoords are inconsistent: {err}')\n\n # Set (merged) info attributes\n for attr in ('name', 'meta', 'description'):\n if attr in attrs:\n setattr(out.info, attr, attrs[attr])\n\n return out\n\n\nclass SkyCoord(ShapedLikeNDArray):\n \"\"\"High-level object providing a flexible interface for celestial coordinate\n representation, manipulation, and transformation between systems.\n\n The `SkyCoord` class accepts a wide variety of inputs for initialization. At\n a minimum these must provide one or more celestial coordinate values with\n unambiguous units. Inputs may be scalars or lists/tuples/arrays, yielding\n scalar or array coordinates (can be checked via ``SkyCoord.isscalar``).\n Typically one also specifies the coordinate frame, though this is not\n required. The general pattern for spherical representations is::\n\n SkyCoord(COORD, [FRAME], keyword_args ...)\n SkyCoord(LON, LAT, [FRAME], keyword_args ...)\n SkyCoord(LON, LAT, [DISTANCE], frame=FRAME, unit=UNIT, keyword_args ...)\n SkyCoord([FRAME], <lon_attr>=LON, <lat_attr>=LAT, keyword_args ...)\n\n It is also possible to input coordinate values in other representations\n such as cartesian or cylindrical. In this case one includes the keyword\n argument ``representation_type='cartesian'`` (for example) along with data\n in ``x``, ``y``, and ``z``.\n\n See also: http://docs.astropy.org/en/stable/coordinates/\n\n Examples\n --------\n The examples below illustrate common ways of initializing a `SkyCoord`\n object. For a complete description of the allowed syntax see the\n full coordinates documentation. First some imports::\n\n >>> from astropy.coordinates import SkyCoord # High-level coordinates\n >>> from astropy.coordinates import ICRS, Galactic, FK4, FK5 # Low-level frames\n >>> from astropy.coordinates import Angle, Latitude, Longitude # Angles\n >>> import astropy.units as u\n\n The coordinate values and frame specification can now be provided using\n positional and keyword arguments::\n\n >>> c = SkyCoord(10, 20, unit=\"deg\") # defaults to ICRS frame\n >>> c = SkyCoord([1, 2, 3], [-30, 45, 8], frame=\"icrs\", unit=\"deg\") # 3 coords\n\n >>> coords = [\"1:12:43.2 +31:12:43\", \"1 12 43.2 +31 12 43\"]\n >>> c = SkyCoord(coords, frame=FK4, unit=(u.hourangle, u.deg), obstime=\"J1992.21\")\n\n >>> c = SkyCoord(\"1h12m43.2s +1d12m43s\", frame=Galactic) # Units from string\n >>> c = SkyCoord(frame=\"galactic\", l=\"1h12m43.2s\", b=\"+1d12m43s\")\n\n >>> ra = Longitude([1, 2, 3], unit=u.deg) # Could also use Angle\n >>> dec = np.array([4.5, 5.2, 6.3]) * u.deg # Astropy Quantity\n >>> c = SkyCoord(ra, dec, frame='icrs')\n >>> c = SkyCoord(frame=ICRS, ra=ra, dec=dec, obstime='2001-01-02T12:34:56')\n\n >>> c = FK4(1 * u.deg, 2 * u.deg) # Uses defaults for obstime, equinox\n >>> c = SkyCoord(c, obstime='J2010.11', equinox='B1965') # Override defaults\n\n >>> c = SkyCoord(w=0, u=1, v=2, unit='kpc', frame='galactic',\n ... representation_type='cartesian')\n\n >>> c = SkyCoord([ICRS(ra=1*u.deg, dec=2*u.deg), ICRS(ra=3*u.deg, dec=4*u.deg)])\n\n Velocity components (proper motions or radial velocities) can also be\n provided in a similar manner::\n\n >>> c = SkyCoord(ra=1*u.deg, dec=2*u.deg, radial_velocity=10*u.km/u.s)\n\n >>> c = SkyCoord(ra=1*u.deg, dec=2*u.deg, pm_ra_cosdec=2*u.mas/u.yr, pm_dec=1*u.mas/u.yr)\n\n As shown, the frame can be a `~astropy.coordinates.BaseCoordinateFrame`\n class or the corresponding string alias. The frame classes that are built in\n to astropy are `ICRS`, `FK5`, `FK4`, `FK4NoETerms`, and `Galactic`.\n The string aliases are simply lower-case versions of the class name, and\n allow for creating a `SkyCoord` object and transforming frames without\n explicitly importing the frame classes.\n\n Parameters\n ----------\n frame : `~astropy.coordinates.BaseCoordinateFrame` class or string, optional\n Type of coordinate frame this `SkyCoord` should represent. Defaults to\n to ICRS if not given or given as None.\n unit : `~astropy.units.Unit`, string, or tuple of :class:`~astropy.units.Unit` or str, optional\n Units for supplied ``LON`` and ``LAT`` values, respectively. If\n only one unit is supplied then it applies to both ``LON`` and\n ``LAT``.\n obstime : valid `~astropy.time.Time` initializer, optional\n Time(s) of observation.\n equinox : valid `~astropy.time.Time` initializer, optional\n Coordinate frame equinox.\n representation_type : str or Representation class\n Specifies the representation, e.g. 'spherical', 'cartesian', or\n 'cylindrical'. This affects the positional args and other keyword args\n which must correspond to the given representation.\n copy : bool, optional\n If `True` (default), a copy of any coordinate data is made. This\n argument can only be passed in as a keyword argument.\n **keyword_args\n Other keyword arguments as applicable for user-defined coordinate frames.\n Common options include:\n\n ra, dec : valid `~astropy.coordinates.Angle` initializer, optional\n RA and Dec for frames where ``ra`` and ``dec`` are keys in the\n frame's ``representation_component_names``, including `ICRS`,\n `FK5`, `FK4`, and `FK4NoETerms`.\n pm_ra_cosdec, pm_dec : `~astropy.units.Quantity`, optional\n Proper motion components, in angle per time units.\n l, b : valid `~astropy.coordinates.Angle` initializer, optional\n Galactic ``l`` and ``b`` for for frames where ``l`` and ``b`` are\n keys in the frame's ``representation_component_names``, including\n the `Galactic` frame.\n pm_l_cosb, pm_b : `~astropy.units.Quantity`, optional\n Proper motion components in the `Galactic` frame, in angle per time\n units.\n x, y, z : float or `~astropy.units.Quantity`, optional\n Cartesian coordinates values\n u, v, w : float or `~astropy.units.Quantity`, optional\n Cartesian coordinates values for the Galactic frame.\n radial_velocity : `~astropy.units.Quantity`, optional\n The component of the velocity along the line-of-sight (i.e., the\n radial direction), in velocity units.\n \"\"\"\n\n # Declare that SkyCoord can be used as a Table column by defining the\n # info property.\n info = SkyCoordInfo()\n\n def __init__(self, *args, copy=True, **kwargs):\n\n # these are frame attributes set on this SkyCoord but *not* a part of\n # the frame object this SkyCoord contains\n self._extra_frameattr_names = set()\n\n # If all that is passed in is a frame instance that already has data,\n # we should bypass all of the parsing and logic below. This is here\n # to make this the fastest way to create a SkyCoord instance. Many of\n # the classmethods implemented for performance enhancements will use\n # this as the initialization path\n if (len(args) == 1 and len(kwargs) == 0\n and isinstance(args[0], (BaseCoordinateFrame, SkyCoord))):\n\n coords = args[0]\n if isinstance(coords, SkyCoord):\n self._extra_frameattr_names = coords._extra_frameattr_names\n self.info = coords.info\n\n # Copy over any extra frame attributes\n for attr_name in self._extra_frameattr_names:\n # Setting it will also validate it.\n setattr(self, attr_name, getattr(coords, attr_name))\n\n coords = coords.frame\n\n if not coords.has_data:\n raise ValueError('Cannot initialize from a coordinate frame '\n 'instance without coordinate data')\n\n if copy:\n self._sky_coord_frame = coords.copy()\n else:\n self._sky_coord_frame = coords\n\n else:\n # Get the frame instance without coordinate data but with all frame\n # attributes set - these could either have been passed in with the\n # frame as an instance, or passed in as kwargs here\n frame_cls, frame_kwargs = _get_frame_without_data(args, kwargs)\n\n # Parse the args and kwargs to assemble a sanitized and validated\n # kwargs dict for initializing attributes for this object and for\n # creating the internal self._sky_coord_frame object\n args = list(args) # Make it mutable\n skycoord_kwargs, components, info = _parse_coordinate_data(\n frame_cls(**frame_kwargs), args, kwargs)\n\n # In the above two parsing functions, these kwargs were identified\n # as valid frame attributes for *some* frame, but not the frame that\n # this SkyCoord will have. We keep these attributes as special\n # skycoord frame attributes:\n for attr in skycoord_kwargs:\n # Setting it will also validate it.\n setattr(self, attr, skycoord_kwargs[attr])\n\n if info is not None:\n self.info = info\n\n # Finally make the internal coordinate object.\n frame_kwargs.update(components)\n self._sky_coord_frame = frame_cls(copy=copy, **frame_kwargs)\n\n if not self._sky_coord_frame.has_data:\n raise ValueError('Cannot create a SkyCoord without data')\n\n @property\n def frame(self):\n return self._sky_coord_frame\n\n @property\n def representation_type(self):\n return self.frame.representation_type\n\n @representation_type.setter\n def representation_type(self, value):\n self.frame.representation_type = value\n\n # TODO: remove these in future\n @property\n def representation(self):\n return self.frame.representation\n\n @representation.setter\n def representation(self, value):\n self.frame.representation = value\n\n @property\n def shape(self):\n return self.frame.shape\n\n def __eq__(self, value):\n \"\"\"Equality operator for SkyCoord\n\n This implements strict equality and requires that the frames are\n equivalent, extra frame attributes are equivalent, and that the\n representation data are exactly equal.\n \"\"\"\n # Make sure that any extra frame attribute names are equivalent.\n for attr in self._extra_frameattr_names | value._extra_frameattr_names:\n if not self.frame._frameattr_equiv(getattr(self, attr),\n getattr(value, attr)):\n raise ValueError(f\"cannot compare: extra frame attribute \"\n f\"'{attr}' is not equivalent \"\n f\"(perhaps compare the frames directly to avoid \"\n f\"this exception)\")\n\n return self._sky_coord_frame == value._sky_coord_frame\n\n def __ne__(self, value):\n return np.logical_not(self == value)\n\n def _apply(self, method, *args, **kwargs):\n \"\"\"Create a new instance, applying a method to the underlying data.\n\n In typical usage, the method is any of the shape-changing methods for\n `~numpy.ndarray` (``reshape``, ``swapaxes``, etc.), as well as those\n picking particular elements (``__getitem__``, ``take``, etc.), which\n are all defined in `~astropy.utils.shapes.ShapedLikeNDArray`. It will be\n applied to the underlying arrays in the representation (e.g., ``x``,\n ``y``, and ``z`` for `~astropy.coordinates.CartesianRepresentation`),\n as well as to any frame attributes that have a shape, with the results\n used to create a new instance.\n\n Internally, it is also used to apply functions to the above parts\n (in particular, `~numpy.broadcast_to`).\n\n Parameters\n ----------\n method : str or callable\n If str, it is the name of a method that is applied to the internal\n ``components``. If callable, the function is applied.\n args : tuple\n Any positional arguments for ``method``.\n kwargs : dict\n Any keyword arguments for ``method``.\n \"\"\"\n def apply_method(value):\n if isinstance(value, ShapedLikeNDArray):\n return value._apply(method, *args, **kwargs)\n else:\n if callable(method):\n return method(value, *args, **kwargs)\n else:\n return getattr(value, method)(*args, **kwargs)\n\n # create a new but empty instance, and copy over stuff\n new = super().__new__(self.__class__)\n new._sky_coord_frame = self._sky_coord_frame._apply(method,\n *args, **kwargs)\n new._extra_frameattr_names = self._extra_frameattr_names.copy()\n for attr in self._extra_frameattr_names:\n value = getattr(self, attr)\n if getattr(value, 'shape', ()):\n value = apply_method(value)\n elif method == 'copy' or method == 'flatten':\n # flatten should copy also for a single element array, but\n # we cannot use it directly for array scalars, since it\n # always returns a one-dimensional array. So, just copy.\n value = copy.copy(value)\n setattr(new, '_' + attr, value)\n\n # Copy other 'info' attr only if it has actually been defined.\n # See PR #3898 for further explanation and justification, along\n # with Quantity.__array_finalize__\n if 'info' in self.__dict__:\n new.info = self.info\n\n return new\n\n def __setitem__(self, item, value):\n \"\"\"Implement self[item] = value for SkyCoord\n\n The right hand ``value`` must be strictly consistent with self:\n - Identical class\n - Equivalent frames\n - Identical representation_types\n - Identical representation differentials keys\n - Identical frame attributes\n - Identical \"extra\" frame attributes (e.g. obstime for an ICRS coord)\n\n With these caveats the setitem ends up as effectively a setitem on\n the representation data.\n\n self.frame.data[item] = value.frame.data\n \"\"\"\n if self.__class__ is not value.__class__:\n raise TypeError(f'can only set from object of same class: '\n f'{self.__class__.__name__} vs. '\n f'{value.__class__.__name__}')\n\n # Make sure that any extra frame attribute names are equivalent.\n for attr in self._extra_frameattr_names | value._extra_frameattr_names:\n if not self.frame._frameattr_equiv(getattr(self, attr),\n getattr(value, attr)):\n raise ValueError(f'attribute {attr} is not equivalent')\n\n # Set the frame values. This checks frame equivalence and also clears\n # the cache to ensure that the object is not in an inconsistent state.\n self._sky_coord_frame[item] = value._sky_coord_frame\n\n def insert(self, obj, values, axis=0):\n \"\"\"\n Insert coordinate values before the given indices in the object and\n return a new Frame object.\n\n The values to be inserted must conform to the rules for in-place setting\n of ``SkyCoord`` objects.\n\n The API signature matches the ``np.insert`` API, but is more limited.\n The specification of insert index ``obj`` must be a single integer,\n and the ``axis`` must be ``0`` for simple insertion before the index.\n\n Parameters\n ----------\n obj : int\n Integer index before which ``values`` is inserted.\n values : array_like\n Value(s) to insert. If the type of ``values`` is different\n from that of quantity, ``values`` is converted to the matching type.\n axis : int, optional\n Axis along which to insert ``values``. Default is 0, which is the\n only allowed value and will insert a row.\n\n Returns\n -------\n out : `~astropy.coordinates.SkyCoord` instance\n New coordinate object with inserted value(s)\n\n \"\"\"\n # Validate inputs: obj arg is integer, axis=0, self is not a scalar, and\n # input index is in bounds.\n try:\n idx0 = operator.index(obj)\n except TypeError:\n raise TypeError('obj arg must be an integer')\n\n if axis != 0:\n raise ValueError('axis must be 0')\n\n if not self.shape:\n raise TypeError('cannot insert into scalar {} object'\n .format(self.__class__.__name__))\n\n if abs(idx0) > len(self):\n raise IndexError('index {} is out of bounds for axis 0 with size {}'\n .format(idx0, len(self)))\n\n # Turn negative index into positive\n if idx0 < 0:\n idx0 = len(self) + idx0\n\n n_values = len(values) if values.shape else 1\n\n # Finally make the new object with the correct length and set values for the\n # three sections, before insert, the insert, and after the insert.\n out = self.__class__.info.new_like([self], len(self) + n_values, name=self.info.name)\n\n # Set the output values. This is where validation of `values` takes place to ensure\n # that it can indeed be inserted.\n out[:idx0] = self[:idx0]\n out[idx0:idx0 + n_values] = values\n out[idx0 + n_values:] = self[idx0:]\n\n return out\n\n def transform_to(self, frame, merge_attributes=True):\n \"\"\"Transform this coordinate to a new frame.\n\n The precise frame transformed to depends on ``merge_attributes``.\n If `False`, the destination frame is used exactly as passed in.\n But this is often not quite what one wants. E.g., suppose one wants to\n transform an ICRS coordinate that has an obstime attribute to FK4; in\n this case, one likely would want to use this information. Thus, the\n default for ``merge_attributes`` is `True`, in which the precedence is\n as follows: (1) explicitly set (i.e., non-default) values in the\n destination frame; (2) explicitly set values in the source; (3) default\n value in the destination frame.\n\n Note that in either case, any explicitly set attributes on the source\n `SkyCoord` that are not part of the destination frame's definition are\n kept (stored on the resulting `SkyCoord`), and thus one can round-trip\n (e.g., from FK4 to ICRS to FK4 without loosing obstime).\n\n Parameters\n ----------\n frame : str, `BaseCoordinateFrame` class or instance, or `SkyCoord` instance\n The frame to transform this coordinate into. If a `SkyCoord`, the\n underlying frame is extracted, and all other information ignored.\n merge_attributes : bool, optional\n Whether the default attributes in the destination frame are allowed\n to be overridden by explicitly set attributes in the source\n (see note above; default: `True`).\n\n Returns\n -------\n coord : `SkyCoord`\n A new object with this coordinate represented in the `frame` frame.\n\n Raises\n ------\n ValueError\n If there is no possible transformation route.\n\n \"\"\"\n from astropy.coordinates.errors import ConvertError\n\n frame_kwargs = {}\n\n # Frame name (string) or frame class? Coerce into an instance.\n try:\n frame = _get_frame_class(frame)()\n except Exception:\n pass\n\n if isinstance(frame, SkyCoord):\n frame = frame.frame # Change to underlying coord frame instance\n\n if isinstance(frame, BaseCoordinateFrame):\n new_frame_cls = frame.__class__\n # Get frame attributes, allowing defaults to be overridden by\n # explicitly set attributes of the source if ``merge_attributes``.\n for attr in frame_transform_graph.frame_attributes:\n self_val = getattr(self, attr, None)\n frame_val = getattr(frame, attr, None)\n if (frame_val is not None\n and not (merge_attributes\n and frame.is_frame_attr_default(attr))):\n frame_kwargs[attr] = frame_val\n elif (self_val is not None\n and not self.is_frame_attr_default(attr)):\n frame_kwargs[attr] = self_val\n elif frame_val is not None:\n frame_kwargs[attr] = frame_val\n else:\n raise ValueError('Transform `frame` must be a frame name, class, or instance')\n\n # Get the composite transform to the new frame\n trans = frame_transform_graph.get_transform(self.frame.__class__, new_frame_cls)\n if trans is None:\n raise ConvertError('Cannot transform from {} to {}'\n .format(self.frame.__class__, new_frame_cls))\n\n # Make a generic frame which will accept all the frame kwargs that\n # are provided and allow for transforming through intermediate frames\n # which may require one or more of those kwargs.\n generic_frame = GenericFrame(frame_kwargs)\n\n # Do the transformation, returning a coordinate frame of the desired\n # final type (not generic).\n new_coord = trans(self.frame, generic_frame)\n\n # Finally make the new SkyCoord object from the `new_coord` and\n # remaining frame_kwargs that are not frame_attributes in `new_coord`.\n for attr in (set(new_coord.get_frame_attr_names()) &\n set(frame_kwargs.keys())):\n frame_kwargs.pop(attr)\n\n return self.__class__(new_coord, **frame_kwargs)\n\n def apply_space_motion(self, new_obstime=None, dt=None):\n \"\"\"\n Compute the position of the source represented by this coordinate object\n to a new time using the velocities stored in this object and assuming\n linear space motion (including relativistic corrections). This is\n sometimes referred to as an \"epoch transformation.\"\n\n The initial time before the evolution is taken from the ``obstime``\n attribute of this coordinate. Note that this method currently does not\n support evolving coordinates where the *frame* has an ``obstime`` frame\n attribute, so the ``obstime`` is only used for storing the before and\n after times, not actually as an attribute of the frame. Alternatively,\n if ``dt`` is given, an ``obstime`` need not be provided at all.\n\n Parameters\n ----------\n new_obstime : `~astropy.time.Time`, optional\n The time at which to evolve the position to. Requires that the\n ``obstime`` attribute be present on this frame.\n dt : `~astropy.units.Quantity`, `~astropy.time.TimeDelta`, optional\n An amount of time to evolve the position of the source. Cannot be\n given at the same time as ``new_obstime``.\n\n Returns\n -------\n new_coord : `SkyCoord`\n A new coordinate object with the evolved location of this coordinate\n at the new time. ``obstime`` will be set on this object to the new\n time only if ``self`` also has ``obstime``.\n \"\"\"\n\n if (new_obstime is None and dt is None or\n new_obstime is not None and dt is not None):\n raise ValueError(\"You must specify one of `new_obstime` or `dt`, \"\n \"but not both.\")\n\n # Validate that we have velocity info\n if 's' not in self.frame.data.differentials:\n raise ValueError('SkyCoord requires velocity data to evolve the '\n 'position.')\n\n if 'obstime' in self.frame.frame_attributes:\n raise NotImplementedError(\"Updating the coordinates in a frame \"\n \"with explicit time dependence is \"\n \"currently not supported. If you would \"\n \"like this functionality, please open an \"\n \"issue on github:\\n\"\n \"https://github.com/astropy/astropy\")\n\n if new_obstime is not None and self.obstime is None:\n # If no obstime is already on this object, raise an error if a new\n # obstime is passed: we need to know the time / epoch at which the\n # the position / velocity were measured initially\n raise ValueError('This object has no associated `obstime`. '\n 'apply_space_motion() must receive a time '\n 'difference, `dt`, and not a new obstime.')\n\n # Compute t1 and t2, the times used in the starpm call, which *only*\n # uses them to compute a delta-time\n t1 = self.obstime\n if dt is None:\n # self.obstime is not None and new_obstime is not None b/c of above\n # checks\n t2 = new_obstime\n else:\n # new_obstime is definitely None b/c of the above checks\n if t1 is None:\n # MAGIC NUMBER: if the current SkyCoord object has no obstime,\n # assume J2000 to do the dt offset. This is not actually used\n # for anything except a delta-t in starpm, so it's OK that it's\n # not necessarily the \"real\" obstime\n t1 = Time('J2000')\n new_obstime = None # we don't actually know the inital obstime\n t2 = t1 + dt\n else:\n t2 = t1 + dt\n new_obstime = t2\n # starpm wants tdb time\n t1 = t1.tdb\n t2 = t2.tdb\n\n # proper motion in RA should not include the cos(dec) term, see the\n # erfa function eraStarpv, comment (4). So we convert to the regular\n # spherical differentials.\n icrsrep = self.icrs.represent_as(SphericalRepresentation, SphericalDifferential)\n icrsvel = icrsrep.differentials['s']\n\n parallax_zero = False\n try:\n plx = icrsrep.distance.to_value(u.arcsecond, u.parallax())\n except u.UnitConversionError: # No distance: set to 0 by convention\n plx = 0.\n parallax_zero = True\n\n try:\n rv = icrsvel.d_distance.to_value(u.km/u.s)\n except u.UnitConversionError: # No RV\n rv = 0.\n\n starpm = erfa.pmsafe(icrsrep.lon.radian, icrsrep.lat.radian,\n icrsvel.d_lon.to_value(u.radian/u.yr),\n icrsvel.d_lat.to_value(u.radian/u.yr),\n plx, rv, t1.jd1, t1.jd2, t2.jd1, t2.jd2)\n\n if parallax_zero:\n new_distance = None\n else:\n new_distance = Distance(parallax=starpm[4] << u.arcsec)\n\n icrs2 = ICRS(ra=u.Quantity(starpm[0], u.radian, copy=False),\n dec=u.Quantity(starpm[1], u.radian, copy=False),\n pm_ra=u.Quantity(starpm[2], u.radian/u.yr, copy=False),\n pm_dec=u.Quantity(starpm[3], u.radian/u.yr, copy=False),\n distance=new_distance,\n radial_velocity=u.Quantity(starpm[5], u.km/u.s, copy=False),\n differential_type=SphericalDifferential)\n\n # Update the obstime of the returned SkyCoord, and need to carry along\n # the frame attributes\n frattrs = {attrnm: getattr(self, attrnm)\n for attrnm in self._extra_frameattr_names}\n frattrs['obstime'] = new_obstime\n return self.__class__(icrs2, **frattrs).transform_to(self.frame)\n\n def _is_name(self, string):\n \"\"\"\n Returns whether a string is one of the aliases for the frame.\n \"\"\"\n return (self.frame.name == string or\n (isinstance(self.frame.name, list) and string in self.frame.name))\n\n def __getattr__(self, attr):\n \"\"\"\n Overrides getattr to return coordinates that this can be transformed\n to, based on the alias attr in the master transform graph.\n \"\"\"\n if '_sky_coord_frame' in self.__dict__:\n if self._is_name(attr):\n return self # Should this be a deepcopy of self?\n\n # Anything in the set of all possible frame_attr_names is handled\n # here. If the attr is relevant for the current frame then delegate\n # to self.frame otherwise get it from self._<attr>.\n if attr in frame_transform_graph.frame_attributes:\n if attr in self.frame.get_frame_attr_names():\n return getattr(self.frame, attr)\n else:\n return getattr(self, '_' + attr, None)\n\n # Some attributes might not fall in the above category but still\n # are available through self._sky_coord_frame.\n if not attr.startswith('_') and hasattr(self._sky_coord_frame, attr):\n return getattr(self._sky_coord_frame, attr)\n\n # Try to interpret as a new frame for transforming.\n frame_cls = frame_transform_graph.lookup_name(attr)\n if frame_cls is not None and self.frame.is_transformable_to(frame_cls):\n return self.transform_to(attr)\n\n # Fail\n raise AttributeError(\"'{}' object has no attribute '{}'\"\n .format(self.__class__.__name__, attr))\n\n def __setattr__(self, attr, val):\n # This is to make anything available through __getattr__ immutable\n if '_sky_coord_frame' in self.__dict__:\n if self._is_name(attr):\n raise AttributeError(f\"'{attr}' is immutable\")\n\n if not attr.startswith('_') and hasattr(self._sky_coord_frame, attr):\n setattr(self._sky_coord_frame, attr, val)\n return\n\n frame_cls = frame_transform_graph.lookup_name(attr)\n if frame_cls is not None and self.frame.is_transformable_to(frame_cls):\n raise AttributeError(f\"'{attr}' is immutable\")\n\n if attr in frame_transform_graph.frame_attributes:\n # All possible frame attributes can be set, but only via a private\n # variable. See __getattr__ above.\n super().__setattr__('_' + attr, val)\n # Validate it\n frame_transform_graph.frame_attributes[attr].__get__(self)\n # And add to set of extra attributes\n self._extra_frameattr_names |= {attr}\n\n else:\n # Otherwise, do the standard Python attribute setting\n super().__setattr__(attr, val)\n\n def __delattr__(self, attr):\n # mirror __setattr__ above\n if '_sky_coord_frame' in self.__dict__:\n if self._is_name(attr):\n raise AttributeError(f\"'{attr}' is immutable\")\n\n if not attr.startswith('_') and hasattr(self._sky_coord_frame,\n attr):\n delattr(self._sky_coord_frame, attr)\n return\n\n frame_cls = frame_transform_graph.lookup_name(attr)\n if frame_cls is not None and self.frame.is_transformable_to(frame_cls):\n raise AttributeError(f\"'{attr}' is immutable\")\n\n if attr in frame_transform_graph.frame_attributes:\n # All possible frame attributes can be deleted, but need to remove\n # the corresponding private variable. See __getattr__ above.\n super().__delattr__('_' + attr)\n # Also remove it from the set of extra attributes\n self._extra_frameattr_names -= {attr}\n\n else:\n # Otherwise, do the standard Python attribute setting\n super().__delattr__(attr)\n\n @override__dir__\n def __dir__(self):\n \"\"\"\n Override the builtin `dir` behavior to include:\n - Transforms available by aliases\n - Attribute / methods of the underlying self.frame object\n \"\"\"\n\n # determine the aliases that this can be transformed to.\n dir_values = set()\n for name in frame_transform_graph.get_names():\n frame_cls = frame_transform_graph.lookup_name(name)\n if self.frame.is_transformable_to(frame_cls):\n dir_values.add(name)\n\n # Add public attributes of self.frame\n dir_values.update(set(attr for attr in dir(self.frame) if not attr.startswith('_')))\n\n # Add all possible frame attributes\n dir_values.update(frame_transform_graph.frame_attributes.keys())\n\n return dir_values\n\n def __repr__(self):\n clsnm = self.__class__.__name__\n coonm = self.frame.__class__.__name__\n frameattrs = self.frame._frame_attrs_repr()\n if frameattrs:\n frameattrs = ': ' + frameattrs\n\n data = self.frame._data_repr()\n if data:\n data = ': ' + data\n\n return '<{clsnm} ({coonm}{frameattrs}){data}>'.format(**locals())\n\n def to_string(self, style='decimal', **kwargs):\n \"\"\"\n A string representation of the coordinates.\n\n The default styles definitions are::\n\n 'decimal': 'lat': {'decimal': True, 'unit': \"deg\"}\n 'lon': {'decimal': True, 'unit': \"deg\"}\n 'dms': 'lat': {'unit': \"deg\"}\n 'lon': {'unit': \"deg\"}\n 'hmsdms': 'lat': {'alwayssign': True, 'pad': True, 'unit': \"deg\"}\n 'lon': {'pad': True, 'unit': \"hour\"}\n\n See :meth:`~astropy.coordinates.Angle.to_string` for details and\n keyword arguments (the two angles forming the coordinates are are\n both :class:`~astropy.coordinates.Angle` instances). Keyword\n arguments have precedence over the style defaults and are passed\n to :meth:`~astropy.coordinates.Angle.to_string`.\n\n Parameters\n ----------\n style : {'hmsdms', 'dms', 'decimal'}\n The formatting specification to use. These encode the three most\n common ways to represent coordinates. The default is `decimal`.\n kwargs\n Keyword args passed to :meth:`~astropy.coordinates.Angle.to_string`.\n \"\"\"\n\n sph_coord = self.frame.represent_as(SphericalRepresentation)\n\n styles = {'hmsdms': {'lonargs': {'unit': u.hour, 'pad': True},\n 'latargs': {'unit': u.degree, 'pad': True, 'alwayssign': True}},\n 'dms': {'lonargs': {'unit': u.degree},\n 'latargs': {'unit': u.degree}},\n 'decimal': {'lonargs': {'unit': u.degree, 'decimal': True},\n 'latargs': {'unit': u.degree, 'decimal': True}}\n }\n\n lonargs = {}\n latargs = {}\n\n if style in styles:\n lonargs.update(styles[style]['lonargs'])\n latargs.update(styles[style]['latargs'])\n else:\n raise ValueError('Invalid style. Valid options are: {}'.format(\",\".join(styles)))\n\n lonargs.update(kwargs)\n latargs.update(kwargs)\n\n if np.isscalar(sph_coord.lon.value):\n coord_string = (sph_coord.lon.to_string(**lonargs) +\n \" \" + sph_coord.lat.to_string(**latargs))\n else:\n coord_string = []\n for lonangle, latangle in zip(sph_coord.lon.ravel(), sph_coord.lat.ravel()):\n coord_string += [(lonangle.to_string(**lonargs) +\n \" \" + latangle.to_string(**latargs))]\n if len(sph_coord.shape) > 1:\n coord_string = np.array(coord_string).reshape(sph_coord.shape)\n\n return coord_string\n\n def is_equivalent_frame(self, other):\n \"\"\"\n Checks if this object's frame as the same as that of the ``other``\n object.\n\n To be the same frame, two objects must be the same frame class and have\n the same frame attributes. For two `SkyCoord` objects, *all* of the\n frame attributes have to match, not just those relevant for the object's\n frame.\n\n Parameters\n ----------\n other : SkyCoord or BaseCoordinateFrame\n The other object to check.\n\n Returns\n -------\n isequiv : bool\n True if the frames are the same, False if not.\n\n Raises\n ------\n TypeError\n If ``other`` isn't a `SkyCoord` or a `BaseCoordinateFrame` or subclass.\n \"\"\"\n if isinstance(other, BaseCoordinateFrame):\n return self.frame.is_equivalent_frame(other)\n elif isinstance(other, SkyCoord):\n if other.frame.name != self.frame.name:\n return False\n\n for fattrnm in frame_transform_graph.frame_attributes:\n if not BaseCoordinateFrame._frameattr_equiv(getattr(self, fattrnm),\n getattr(other, fattrnm)):\n return False\n return True\n else:\n # not a BaseCoordinateFrame nor a SkyCoord object\n raise TypeError(\"Tried to do is_equivalent_frame on something that \"\n \"isn't frame-like\")\n\n # High-level convenience methods\n def separation(self, other):\n \"\"\"\n Computes on-sky separation between this coordinate and another.\n\n .. note::\n\n If the ``other`` coordinate object is in a different frame, it is\n first transformed to the frame of this object. This can lead to\n unintuitive behavior if not accounted for. Particularly of note is\n that ``self.separation(other)`` and ``other.separation(self)`` may\n not give the same answer in this case.\n\n For more on how to use this (and related) functionality, see the\n examples in :doc:`/coordinates/matchsep`.\n\n Parameters\n ----------\n other : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\n The coordinate to get the separation to.\n\n Returns\n -------\n sep : `~astropy.coordinates.Angle`\n The on-sky separation between this and the ``other`` coordinate.\n\n Notes\n -----\n The separation is calculated using the Vincenty formula, which\n is stable at all locations, including poles and antipodes [1]_.\n\n .. [1] https://en.wikipedia.org/wiki/Great-circle_distance\n\n \"\"\"\n from . import Angle\n from .angle_utilities import angular_separation\n\n if not self.is_equivalent_frame(other):\n try:\n kwargs = {'merge_attributes': False} if isinstance(other, SkyCoord) else {}\n other = other.transform_to(self, **kwargs)\n except TypeError:\n raise TypeError('Can only get separation to another SkyCoord '\n 'or a coordinate frame with data')\n\n lon1 = self.spherical.lon\n lat1 = self.spherical.lat\n lon2 = other.spherical.lon\n lat2 = other.spherical.lat\n\n # Get the separation as a Quantity, convert to Angle in degrees\n sep = angular_separation(lon1, lat1, lon2, lat2)\n return Angle(sep, unit=u.degree)\n\n def separation_3d(self, other):\n \"\"\"\n Computes three dimensional separation between this coordinate\n and another.\n\n For more on how to use this (and related) functionality, see the\n examples in :doc:`/coordinates/matchsep`.\n\n Parameters\n ----------\n other : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\n The coordinate to get the separation to.\n\n Returns\n -------\n sep : `~astropy.coordinates.Distance`\n The real-space distance between these two coordinates.\n\n Raises\n ------\n ValueError\n If this or the other coordinate do not have distances.\n \"\"\"\n if not self.is_equivalent_frame(other):\n try:\n kwargs = {'merge_attributes': False} if isinstance(other, SkyCoord) else {}\n other = other.transform_to(self, **kwargs)\n except TypeError:\n raise TypeError('Can only get separation to another SkyCoord '\n 'or a coordinate frame with data')\n\n if issubclass(self.data.__class__, UnitSphericalRepresentation):\n raise ValueError('This object does not have a distance; cannot '\n 'compute 3d separation.')\n if issubclass(other.data.__class__, UnitSphericalRepresentation):\n raise ValueError('The other object does not have a distance; '\n 'cannot compute 3d separation.')\n\n c1 = self.cartesian.without_differentials()\n c2 = other.cartesian.without_differentials()\n return Distance((c1 - c2).norm())\n\n def spherical_offsets_to(self, tocoord):\n r\"\"\"\n Computes angular offsets to go *from* this coordinate *to* another.\n\n Parameters\n ----------\n tocoord : `~astropy.coordinates.BaseCoordinateFrame`\n The coordinate to find the offset to.\n\n Returns\n -------\n lon_offset : `~astropy.coordinates.Angle`\n The angular offset in the longitude direction (i.e., RA for\n equatorial coordinates).\n lat_offset : `~astropy.coordinates.Angle`\n The angular offset in the latitude direction (i.e., Dec for\n equatorial coordinates).\n\n Raises\n ------\n ValueError\n If the ``tocoord`` is not in the same frame as this one. This is\n different from the behavior of the `separation`/`separation_3d`\n methods because the offset components depend critically on the\n specific choice of frame.\n\n Notes\n -----\n This uses the sky offset frame machinery, and hence will produce a new\n sky offset frame if one does not already exist for this object's frame\n class.\n\n See Also\n --------\n separation : for the *total* angular offset (not broken out into components).\n position_angle : for the direction of the offset.\n\n \"\"\"\n if not self.is_equivalent_frame(tocoord):\n raise ValueError('Tried to use spherical_offsets_to with two non-matching frames!')\n\n aframe = self.skyoffset_frame()\n acoord = tocoord.transform_to(aframe)\n\n dlon = acoord.spherical.lon.view(Angle)\n dlat = acoord.spherical.lat.view(Angle)\n return dlon, dlat\n\n def directional_offset_by(self, position_angle, separation):\n \"\"\"\n Computes coordinates at the given offset from this coordinate.\n\n Parameters\n ----------\n position_angle : `~astropy.coordinates.Angle`\n position_angle of offset\n separation : `~astropy.coordinates.Angle`\n offset angular separation\n\n Returns\n -------\n newpoints : `~astropy.coordinates.SkyCoord`\n The coordinates for the location that corresponds to offsetting by\n the given `position_angle` and `separation`.\n\n Notes\n -----\n Returned SkyCoord frame retains only the frame attributes that are for\n the resulting frame type. (e.g. if the input frame is\n `~astropy.coordinates.ICRS`, an ``equinox`` value will be retained, but\n an ``obstime`` will not.)\n\n For a more complete set of transform offsets, use `~astropy.wcs.WCS`.\n `~astropy.coordinates.SkyCoord.skyoffset_frame()` can also be used to\n create a spherical frame with (lat=0, lon=0) at a reference point,\n approximating an xy cartesian system for small offsets. This method\n is distinct in that it is accurate on the sphere.\n\n See Also\n --------\n position_angle : inverse operation for the ``position_angle`` component\n separation : inverse operation for the ``separation`` component\n\n \"\"\"\n from . import angle_utilities\n\n slat = self.represent_as(UnitSphericalRepresentation).lat\n slon = self.represent_as(UnitSphericalRepresentation).lon\n\n newlon, newlat = angle_utilities.offset_by(\n lon=slon, lat=slat,\n posang=position_angle, distance=separation)\n\n return SkyCoord(newlon, newlat, frame=self.frame)\n\n def match_to_catalog_sky(self, catalogcoord, nthneighbor=1):\n \"\"\"\n Finds the nearest on-sky matches of this coordinate in a set of\n catalog coordinates.\n\n For more on how to use this (and related) functionality, see the\n examples in :doc:`/coordinates/matchsep`.\n\n Parameters\n ----------\n catalogcoord : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\n The base catalog in which to search for matches. Typically this\n will be a coordinate object that is an array (i.e.,\n ``catalogcoord.isscalar == False``)\n nthneighbor : int, optional\n Which closest neighbor to search for. Typically ``1`` is\n desired here, as that is correct for matching one set of\n coordinates to another. The next likely use case is ``2``,\n for matching a coordinate catalog against *itself* (``1``\n is inappropriate because each point will find itself as the\n closest match).\n\n Returns\n -------\n idx : integer array\n Indices into ``catalogcoord`` to get the matched points for\n each of this object's coordinates. Shape matches this\n object.\n sep2d : `~astropy.coordinates.Angle`\n The on-sky separation between the closest match for each\n element in this object in ``catalogcoord``. Shape matches\n this object.\n dist3d : `~astropy.units.Quantity`\n The 3D distance between the closest match for each element\n in this object in ``catalogcoord``. Shape matches this\n object. Unless both this and ``catalogcoord`` have associated\n distances, this quantity assumes that all sources are at a\n distance of 1 (dimensionless).\n\n Notes\n -----\n This method requires `SciPy <https://www.scipy.org/>`_ to be\n installed or it will fail.\n\n See Also\n --------\n astropy.coordinates.match_coordinates_sky\n SkyCoord.match_to_catalog_3d\n \"\"\"\n from .matching import match_coordinates_sky\n\n if (isinstance(catalogcoord, (SkyCoord, BaseCoordinateFrame))\n and catalogcoord.has_data):\n self_in_catalog_frame = self.transform_to(catalogcoord)\n else:\n raise TypeError('Can only get separation to another SkyCoord or a '\n 'coordinate frame with data')\n\n res = match_coordinates_sky(self_in_catalog_frame, catalogcoord,\n nthneighbor=nthneighbor,\n storekdtree='_kdtree_sky')\n return res\n\n def match_to_catalog_3d(self, catalogcoord, nthneighbor=1):\n \"\"\"\n Finds the nearest 3-dimensional matches of this coordinate to a set\n of catalog coordinates.\n\n This finds the 3-dimensional closest neighbor, which is only different\n from the on-sky distance if ``distance`` is set in this object or the\n ``catalogcoord`` object.\n\n For more on how to use this (and related) functionality, see the\n examples in :doc:`/coordinates/matchsep`.\n\n Parameters\n ----------\n catalogcoord : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\n The base catalog in which to search for matches. Typically this\n will be a coordinate object that is an array (i.e.,\n ``catalogcoord.isscalar == False``)\n nthneighbor : int, optional\n Which closest neighbor to search for. Typically ``1`` is\n desired here, as that is correct for matching one set of\n coordinates to another. The next likely use case is\n ``2``, for matching a coordinate catalog against *itself*\n (``1`` is inappropriate because each point will find\n itself as the closest match).\n\n Returns\n -------\n idx : integer array\n Indices into ``catalogcoord`` to get the matched points for\n each of this object's coordinates. Shape matches this\n object.\n sep2d : `~astropy.coordinates.Angle`\n The on-sky separation between the closest match for each\n element in this object in ``catalogcoord``. Shape matches\n this object.\n dist3d : `~astropy.units.Quantity`\n The 3D distance between the closest match for each element\n in this object in ``catalogcoord``. Shape matches this\n object.\n\n Notes\n -----\n This method requires `SciPy <https://www.scipy.org/>`_ to be\n installed or it will fail.\n\n See Also\n --------\n astropy.coordinates.match_coordinates_3d\n SkyCoord.match_to_catalog_sky\n \"\"\"\n from .matching import match_coordinates_3d\n\n if (isinstance(catalogcoord, (SkyCoord, BaseCoordinateFrame))\n and catalogcoord.has_data):\n self_in_catalog_frame = self.transform_to(catalogcoord)\n else:\n raise TypeError('Can only get separation to another SkyCoord or a '\n 'coordinate frame with data')\n\n res = match_coordinates_3d(self_in_catalog_frame, catalogcoord,\n nthneighbor=nthneighbor,\n storekdtree='_kdtree_3d')\n\n return res\n\n def search_around_sky(self, searcharoundcoords, seplimit):\n \"\"\"\n Searches for all coordinates in this object around a supplied set of\n points within a given on-sky separation.\n\n This is intended for use on `~astropy.coordinates.SkyCoord` objects\n with coordinate arrays, rather than a scalar coordinate. For a scalar\n coordinate, it is better to use\n `~astropy.coordinates.SkyCoord.separation`.\n\n For more on how to use this (and related) functionality, see the\n examples in :doc:`/coordinates/matchsep`.\n\n Parameters\n ----------\n searcharoundcoords : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\n The coordinates to search around to try to find matching points in\n this `SkyCoord`. This should be an object with array coordinates,\n not a scalar coordinate object.\n seplimit : `~astropy.units.Quantity` with angle units\n The on-sky separation to search within.\n\n Returns\n -------\n idxsearcharound : integer array\n Indices into ``searcharoundcoords`` that match the\n corresponding elements of ``idxself``. Shape matches\n ``idxself``.\n idxself : integer array\n Indices into ``self`` that match the\n corresponding elements of ``idxsearcharound``. Shape matches\n ``idxsearcharound``.\n sep2d : `~astropy.coordinates.Angle`\n The on-sky separation between the coordinates. Shape matches\n ``idxsearcharound`` and ``idxself``.\n dist3d : `~astropy.units.Quantity`\n The 3D distance between the coordinates. Shape matches\n ``idxsearcharound`` and ``idxself``.\n\n Notes\n -----\n This method requires `SciPy <https://www.scipy.org/>`_ (>=0.12.0) to be\n installed or it will fail.\n\n In the current implementation, the return values are always sorted in\n the same order as the ``searcharoundcoords`` (so ``idxsearcharound`` is\n in ascending order). This is considered an implementation detail,\n though, so it could change in a future release.\n\n See Also\n --------\n astropy.coordinates.search_around_sky\n SkyCoord.search_around_3d\n \"\"\"\n from .matching import search_around_sky\n\n return search_around_sky(searcharoundcoords, self, seplimit,\n storekdtree='_kdtree_sky')\n\n def search_around_3d(self, searcharoundcoords, distlimit):\n \"\"\"\n Searches for all coordinates in this object around a supplied set of\n points within a given 3D radius.\n\n This is intended for use on `~astropy.coordinates.SkyCoord` objects\n with coordinate arrays, rather than a scalar coordinate. For a scalar\n coordinate, it is better to use\n `~astropy.coordinates.SkyCoord.separation_3d`.\n\n For more on how to use this (and related) functionality, see the\n examples in :doc:`/coordinates/matchsep`.\n\n Parameters\n ----------\n searcharoundcoords : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\n The coordinates to search around to try to find matching points in\n this `SkyCoord`. This should be an object with array coordinates,\n not a scalar coordinate object.\n distlimit : `~astropy.units.Quantity` with distance units\n The physical radius to search within.\n\n Returns\n -------\n idxsearcharound : integer array\n Indices into ``searcharoundcoords`` that match the\n corresponding elements of ``idxself``. Shape matches\n ``idxself``.\n idxself : integer array\n Indices into ``self`` that match the\n corresponding elements of ``idxsearcharound``. Shape matches\n ``idxsearcharound``.\n sep2d : `~astropy.coordinates.Angle`\n The on-sky separation between the coordinates. Shape matches\n ``idxsearcharound`` and ``idxself``.\n dist3d : `~astropy.units.Quantity`\n The 3D distance between the coordinates. Shape matches\n ``idxsearcharound`` and ``idxself``.\n\n Notes\n -----\n This method requires `SciPy <https://www.scipy.org/>`_ (>=0.12.0) to be\n installed or it will fail.\n\n In the current implementation, the return values are always sorted in\n the same order as the ``searcharoundcoords`` (so ``idxsearcharound`` is\n in ascending order). This is considered an implementation detail,\n though, so it could change in a future release.\n\n See Also\n --------\n astropy.coordinates.search_around_3d\n SkyCoord.search_around_sky\n \"\"\"\n from .matching import search_around_3d\n\n return search_around_3d(searcharoundcoords, self, distlimit,\n storekdtree='_kdtree_3d')\n\n def position_angle(self, other):\n \"\"\"\n Computes the on-sky position angle (East of North) between this\n `SkyCoord` and another.\n\n Parameters\n ----------\n other : `SkyCoord`\n The other coordinate to compute the position angle to. It is\n treated as the \"head\" of the vector of the position angle.\n\n Returns\n -------\n pa : `~astropy.coordinates.Angle`\n The (positive) position angle of the vector pointing from ``self``\n to ``other``. If either ``self`` or ``other`` contain arrays, this\n will be an array following the appropriate `numpy` broadcasting\n rules.\n\n Examples\n --------\n\n >>> c1 = SkyCoord(0*u.deg, 0*u.deg)\n >>> c2 = SkyCoord(1*u.deg, 0*u.deg)\n >>> c1.position_angle(c2).degree\n 90.0\n >>> c3 = SkyCoord(1*u.deg, 1*u.deg)\n >>> c1.position_angle(c3).degree # doctest: +FLOAT_CMP\n 44.995636455344844\n \"\"\"\n from . import angle_utilities\n\n if not self.is_equivalent_frame(other):\n try:\n other = other.transform_to(self, merge_attributes=False)\n except TypeError:\n raise TypeError('Can only get position_angle to another '\n 'SkyCoord or a coordinate frame with data')\n\n slat = self.represent_as(UnitSphericalRepresentation).lat\n slon = self.represent_as(UnitSphericalRepresentation).lon\n olat = other.represent_as(UnitSphericalRepresentation).lat\n olon = other.represent_as(UnitSphericalRepresentation).lon\n\n return angle_utilities.position_angle(slon, slat, olon, olat)\n\n def skyoffset_frame(self, rotation=None):\n \"\"\"\n Returns the sky offset frame with this `SkyCoord` at the origin.\n\n Returns\n -------\n astrframe : `~astropy.coordinates.SkyOffsetFrame`\n A sky offset frame of the same type as this `SkyCoord` (e.g., if\n this object has an ICRS coordinate, the resulting frame is\n SkyOffsetICRS, with the origin set to this object)\n rotation : `~astropy.coordinates.Angle` or `~astropy.units.Quantity` with angle units\n The final rotation of the frame about the ``origin``. The sign of\n the rotation is the left-hand rule. That is, an object at a\n particular position angle in the un-rotated system will be sent to\n the positive latitude (z) direction in the final frame.\n \"\"\"\n return SkyOffsetFrame(origin=self, rotation=rotation)\n\n def get_constellation(self, short_name=False, constellation_list='iau'):\n \"\"\"\n Determines the constellation(s) of the coordinates this `SkyCoord`\n contains.\n\n Parameters\n ----------\n short_name : bool\n If True, the returned names are the IAU-sanctioned abbreviated\n names. Otherwise, full names for the constellations are used.\n constellation_list : str\n The set of constellations to use. Currently only ``'iau'`` is\n supported, meaning the 88 \"modern\" constellations endorsed by the IAU.\n\n Returns\n -------\n constellation : str or string array\n If this is a scalar coordinate, returns the name of the\n constellation. If it is an array `SkyCoord`, it returns an array of\n names.\n\n Notes\n -----\n To determine which constellation a point on the sky is in, this first\n precesses to B1875, and then uses the Delporte boundaries of the 88\n modern constellations, as tabulated by\n `Roman 1987 <http://cdsarc.u-strasbg.fr/viz-bin/Cat?VI/42>`_.\n\n See Also\n --------\n astropy.coordinates.get_constellation\n \"\"\"\n from .funcs import get_constellation\n\n # because of issue #7028, the conversion to a PrecessedGeocentric\n # system fails in some cases. Work around is to drop the velocities.\n # they are not needed here since only position infromation is used\n extra_frameattrs = {nm: getattr(self, nm)\n for nm in self._extra_frameattr_names}\n novel = SkyCoord(self.realize_frame(self.data.without_differentials()),\n **extra_frameattrs)\n return get_constellation(novel, short_name, constellation_list)\n\n # the simpler version below can be used when gh-issue #7028 is resolved\n # return get_constellation(self, short_name, constellation_list)\n\n # WCS pixel to/from sky conversions\n def to_pixel(self, wcs, origin=0, mode='all'):\n \"\"\"\n Convert this coordinate to pixel coordinates using a `~astropy.wcs.WCS`\n object.\n\n Parameters\n ----------\n wcs : `~astropy.wcs.WCS`\n The WCS to use for convert\n origin : int\n Whether to return 0 or 1-based pixel coordinates.\n mode : 'all' or 'wcs'\n Whether to do the transformation including distortions (``'all'``) or\n only including only the core WCS transformation (``'wcs'``).\n\n Returns\n -------\n xp, yp : `numpy.ndarray`\n The pixel coordinates\n\n See Also\n --------\n astropy.wcs.utils.skycoord_to_pixel : the implementation of this method\n \"\"\"\n from astropy.wcs.utils import skycoord_to_pixel\n return skycoord_to_pixel(self, wcs=wcs, origin=origin, mode=mode)\n\n @classmethod\n def from_pixel(cls, xp, yp, wcs, origin=0, mode='all'):\n \"\"\"\n Create a new `SkyCoord` from pixel coordinates using an\n `~astropy.wcs.WCS` object.\n\n Parameters\n ----------\n xp, yp : float or `numpy.ndarray`\n The coordinates to convert.\n wcs : `~astropy.wcs.WCS`\n The WCS to use for convert\n origin : int\n Whether to return 0 or 1-based pixel coordinates.\n mode : 'all' or 'wcs'\n Whether to do the transformation including distortions (``'all'``) or\n only including only the core WCS transformation (``'wcs'``).\n\n Returns\n -------\n coord : an instance of this class\n A new object with sky coordinates corresponding to the input ``xp``\n and ``yp``.\n\n See Also\n --------\n to_pixel : to do the inverse operation\n astropy.wcs.utils.pixel_to_skycoord : the implementation of this method\n \"\"\"\n from astropy.wcs.utils import pixel_to_skycoord\n return pixel_to_skycoord(xp, yp, wcs=wcs, origin=origin, mode=mode, cls=cls)\n\n def contained_by(self, wcs, image=None, **kwargs):\n \"\"\"\n Determines if the SkyCoord is contained in the given wcs footprint.\n\n Parameters\n ----------\n wcs : `~astropy.wcs.WCS`\n The coordinate to check if it is within the wcs coordinate.\n image : array\n Optional. The image associated with the wcs object that the cooordinate\n is being checked against. If not given the naxis keywords will be used\n to determine if the coordinate falls within the wcs footprint.\n **kwargs :\n Additional arguments to pass to `~astropy.coordinates.SkyCoord.to_pixel`\n\n Returns\n -------\n response : bool\n True means the WCS footprint contains the coordinate, False means it does not.\n \"\"\"\n\n if image is not None:\n ymax, xmax = image.shape\n else:\n xmax, ymax = wcs._naxis\n\n import warnings\n with warnings.catch_warnings():\n # Suppress warnings since they just mean we didn't find the coordinate\n warnings.simplefilter(\"ignore\")\n try:\n x, y = self.to_pixel(wcs, **kwargs)\n except Exception:\n return False\n\n return (x < xmax) & (x > 0) & (y < ymax) & (y > 0)\n\n def radial_velocity_correction(self, kind='barycentric', obstime=None,\n location=None):\n \"\"\"\n Compute the correction required to convert a radial velocity at a given\n time and place on the Earth's Surface to a barycentric or heliocentric\n velocity.\n\n Parameters\n ----------\n kind : str\n The kind of velocity correction. Must be 'barycentric' or\n 'heliocentric'.\n obstime : `~astropy.time.Time` or None, optional\n The time at which to compute the correction. If `None`, the\n ``obstime`` frame attribute on the `SkyCoord` will be used.\n location : `~astropy.coordinates.EarthLocation` or None, optional\n The observer location at which to compute the correction. If\n `None`, the ``location`` frame attribute on the passed-in\n ``obstime`` will be used, and if that is None, the ``location``\n frame attribute on the `SkyCoord` will be used.\n\n Raises\n ------\n ValueError\n If either ``obstime`` or ``location`` are passed in (not ``None``)\n when the frame attribute is already set on this `SkyCoord`.\n TypeError\n If ``obstime`` or ``location`` aren't provided, either as arguments\n or as frame attributes.\n\n Returns\n -------\n vcorr : `~astropy.units.Quantity` with velocity units\n The correction with a positive sign. I.e., *add* this\n to an observed radial velocity to get the barycentric (or\n heliocentric) velocity. If m/s precision or better is needed,\n see the notes below.\n\n Notes\n -----\n The barycentric correction is calculated to higher precision than the\n heliocentric correction and includes additional physics (e.g time dilation).\n Use barycentric corrections if m/s precision is required.\n\n The algorithm here is sufficient to perform corrections at the mm/s level, but\n care is needed in application. The barycentric correction returned uses the optical\n approximation v = z * c. Strictly speaking, the barycentric correction is\n multiplicative and should be applied as::\n\n >>> from astropy.time import Time\n >>> from astropy.coordinates import SkyCoord, EarthLocation\n >>> from astropy.constants import c\n >>> t = Time(56370.5, format='mjd', scale='utc')\n >>> loc = EarthLocation('149d33m00.5s','-30d18m46.385s',236.87*u.m)\n >>> sc = SkyCoord(1*u.deg, 2*u.deg)\n >>> vcorr = sc.radial_velocity_correction(kind='barycentric', obstime=t, location=loc) # doctest: +REMOTE_DATA\n >>> rv = rv + vcorr + rv * vcorr / c # doctest: +SKIP\n\n Also note that this method returns the correction velocity in the so-called\n *optical convention*::\n\n >>> vcorr = zb * c # doctest: +SKIP\n\n where ``zb`` is the barycentric correction redshift as defined in section 3\n of Wright & Eastman (2014). The application formula given above follows from their\n equation (11) under assumption that the radial velocity ``rv`` has also been defined\n using the same optical convention. Note, this can be regarded as a matter of\n velocity definition and does not by itself imply any loss of accuracy, provided\n sufficient care has been taken during interpretation of the results. If you need\n the barycentric correction expressed as the full relativistic velocity (e.g., to provide\n it as the input to another software which performs the application), the\n following recipe can be used::\n\n >>> zb = vcorr / c # doctest: +REMOTE_DATA\n >>> zb_plus_one_squared = (zb + 1) ** 2 # doctest: +REMOTE_DATA\n >>> vcorr_rel = c * (zb_plus_one_squared - 1) / (zb_plus_one_squared + 1) # doctest: +REMOTE_DATA\n\n or alternatively using just equivalencies::\n\n >>> vcorr_rel = vcorr.to(u.Hz, u.doppler_optical(1*u.Hz)).to(vcorr.unit, u.doppler_relativistic(1*u.Hz)) # doctest: +REMOTE_DATA\n\n See also `~astropy.units.equivalencies.doppler_optical`,\n `~astropy.units.equivalencies.doppler_radio`, and\n `~astropy.units.equivalencies.doppler_relativistic` for more information on\n the velocity conventions.\n\n The default is for this method to use the builtin ephemeris for\n computing the sun and earth location. Other ephemerides can be chosen\n by setting the `~astropy.coordinates.solar_system_ephemeris` variable,\n either directly or via ``with`` statement. For example, to use the JPL\n ephemeris, do::\n\n >>> from astropy.coordinates import solar_system_ephemeris\n >>> sc = SkyCoord(1*u.deg, 2*u.deg)\n >>> with solar_system_ephemeris.set('jpl'): # doctest: +REMOTE_DATA\n ... rv += sc.radial_velocity_correction(obstime=t, location=loc) # doctest: +SKIP\n\n \"\"\"\n # has to be here to prevent circular imports\n from .solar_system import get_body_barycentric_posvel\n\n # location validation\n timeloc = getattr(obstime, 'location', None)\n if location is None:\n if self.location is not None:\n location = self.location\n if timeloc is not None:\n raise ValueError('`location` cannot be in both the '\n 'passed-in `obstime` and this `SkyCoord` '\n 'because it is ambiguous which is meant '\n 'for the radial_velocity_correction.')\n elif timeloc is not None:\n location = timeloc\n else:\n raise TypeError('Must provide a `location` to '\n 'radial_velocity_correction, either as a '\n 'SkyCoord frame attribute, as an attribute on '\n 'the passed in `obstime`, or in the method '\n 'call.')\n\n elif self.location is not None or timeloc is not None:\n raise ValueError('Cannot compute radial velocity correction if '\n '`location` argument is passed in and there is '\n 'also a `location` attribute on this SkyCoord or '\n 'the passed-in `obstime`.')\n\n # obstime validation\n coo_at_rv_obstime = self # assume we need no space motion for now\n if obstime is None:\n obstime = self.obstime\n if obstime is None:\n raise TypeError('Must provide an `obstime` to '\n 'radial_velocity_correction, either as a '\n 'SkyCoord frame attribute or in the method '\n 'call.')\n elif self.obstime is not None and self.frame.data.differentials:\n # we do need space motion after all\n coo_at_rv_obstime = self.apply_space_motion(obstime)\n elif self.obstime is None:\n # warn the user if the object has differentials set\n if 's' in self.data.differentials:\n warnings.warn(\n \"SkyCoord has space motion, and therefore the specified \"\n \"position of the SkyCoord may not be the same as \"\n \"the `obstime` for the radial velocity measurement. \"\n \"This may affect the rv correction at the order of km/s\"\n \"for very high proper motions sources. If you wish to \"\n \"apply space motion of the SkyCoord to correct for this\"\n \"the `obstime` attribute of the SkyCoord must be set\",\n AstropyUserWarning\n )\n\n pos_earth, v_earth = get_body_barycentric_posvel('earth', obstime)\n if kind == 'barycentric':\n v_origin_to_earth = v_earth\n elif kind == 'heliocentric':\n v_sun = get_body_barycentric_posvel('sun', obstime)[1]\n v_origin_to_earth = v_earth - v_sun\n else:\n raise ValueError(\"`kind` argument to radial_velocity_correction must \"\n \"be 'barycentric' or 'heliocentric', but got \"\n \"'{}'\".format(kind))\n\n gcrs_p, gcrs_v = location.get_gcrs_posvel(obstime)\n # transforming to GCRS is not the correct thing to do here, since we don't want to\n # include aberration (or light deflection)? Instead, only apply parallax if necessary\n icrs_cart = coo_at_rv_obstime.icrs.cartesian\n icrs_cart_novel = icrs_cart.without_differentials()\n if self.data.__class__ is UnitSphericalRepresentation:\n targcart = icrs_cart_novel\n else:\n # skycoord has distances so apply parallax\n obs_icrs_cart = pos_earth + gcrs_p\n targcart = icrs_cart_novel - obs_icrs_cart\n targcart /= targcart.norm()\n\n if kind == 'barycentric':\n beta_obs = (v_origin_to_earth + gcrs_v) / speed_of_light\n gamma_obs = 1 / np.sqrt(1 - beta_obs.norm()**2)\n gr = location.gravitational_redshift(obstime)\n # barycentric redshift according to eq 28 in Wright & Eastmann (2014),\n # neglecting Shapiro delay and effects of the star's own motion\n zb = gamma_obs * (1 + beta_obs.dot(targcart)) / (1 + gr/speed_of_light)\n # try and get terms corresponding to stellar motion.\n if icrs_cart.differentials:\n try:\n ro = self.icrs.cartesian\n beta_star = ro.differentials['s'].to_cartesian() / speed_of_light\n # ICRS unit vector at coordinate epoch\n ro = ro.without_differentials()\n ro /= ro.norm()\n zb *= (1 + beta_star.dot(ro)) / (1 + beta_star.dot(targcart))\n except u.UnitConversionError:\n warnings.warn(\"SkyCoord contains some velocity information, but not enough to \"\n \"calculate the full space motion of the source, and so this has \"\n \"been ignored for the purposes of calculating the radial velocity \"\n \"correction. This can lead to errors on the order of metres/second.\",\n AstropyUserWarning)\n\n zb = zb - 1\n return zb * speed_of_light\n else:\n # do a simpler correction ignoring time dilation and gravitational redshift\n # this is adequate since Heliocentric corrections shouldn't be used if\n # cm/s precision is required.\n return targcart.dot(v_origin_to_earth + gcrs_v)\n\n # Table interactions\n @classmethod\n def guess_from_table(cls, table, **coord_kwargs):\n r\"\"\"\n A convenience method to create and return a new `SkyCoord` from the data\n in an astropy Table.\n\n This method matches table columns that start with the case-insensitive\n names of the the components of the requested frames, if they are also\n followed by a non-alphanumeric character. It will also match columns\n that *end* with the component name if a non-alphanumeric character is\n *before* it.\n\n For example, the first rule means columns with names like\n ``'RA[J2000]'`` or ``'ra'`` will be interpreted as ``ra`` attributes for\n `~astropy.coordinates.ICRS` frames, but ``'RAJ2000'`` or ``'radius'``\n are *not*. Similarly, the second rule applied to the\n `~astropy.coordinates.Galactic` frame means that a column named\n ``'gal_l'`` will be used as the the ``l`` component, but ``gall`` or\n ``'fill'`` will not.\n\n The definition of alphanumeric here is based on Unicode's definition\n of alphanumeric, except without ``_`` (which is normally considered\n alphanumeric). So for ASCII, this means the non-alphanumeric characters\n are ``<space>_!\"#$%&'()*+,-./\\:;<=>?@[]^`{|}~``).\n\n Parameters\n ----------\n table : astropy.Table\n The table to load data from.\n coord_kwargs\n Any additional keyword arguments are passed directly to this class's\n constructor.\n\n Returns\n -------\n newsc : same as this class\n The new `SkyCoord` (or subclass) object.\n \"\"\"\n _frame_cls, _frame_kwargs = _get_frame_without_data([], coord_kwargs)\n frame = _frame_cls(**_frame_kwargs)\n coord_kwargs['frame'] = coord_kwargs.get('frame', frame)\n\n comp_kwargs = {}\n for comp_name in frame.representation_component_names:\n # this matches things like 'ra[...]'' but *not* 'rad'.\n # note that the \"_\" must be in there explicitly, because\n # \"alphanumeric\" usually includes underscores.\n starts_with_comp = comp_name + r'(\\W|\\b|_)'\n # this part matches stuff like 'center_ra', but *not*\n # 'aura'\n ends_with_comp = r'.*(\\W|\\b|_)' + comp_name + r'\\b'\n # the final regex ORs together the two patterns\n rex = re.compile('(' + starts_with_comp + ')|(' + ends_with_comp + ')',\n re.IGNORECASE | re.UNICODE)\n\n for col_name in table.colnames:\n if rex.match(col_name):\n if comp_name in comp_kwargs:\n oldname = comp_kwargs[comp_name].name\n msg = ('Found at least two matches for component \"{0}\"'\n ': \"{1}\" and \"{2}\". Cannot continue with this '\n 'ambiguity.')\n raise ValueError(msg.format(comp_name, oldname, col_name))\n comp_kwargs[comp_name] = table[col_name]\n\n for k, v in comp_kwargs.items():\n if k in coord_kwargs:\n raise ValueError('Found column \"{}\" in table, but it was '\n 'already provided as \"{}\" keyword to '\n 'guess_from_table function.'.format(v.name, k))\n else:\n coord_kwargs[k] = v\n\n return cls(**coord_kwargs)\n\n # Name resolve\n @classmethod\n def from_name(cls, name, frame='icrs', parse=False, cache=True):\n \"\"\"\n Given a name, query the CDS name resolver to attempt to retrieve\n coordinate information for that object. The search database, sesame\n url, and query timeout can be set through configuration items in\n ``astropy.coordinates.name_resolve`` -- see docstring for\n `~astropy.coordinates.get_icrs_coordinates` for more\n information.\n\n Parameters\n ----------\n name : str\n The name of the object to get coordinates for, e.g. ``'M42'``.\n frame : str or `BaseCoordinateFrame` class or instance\n The frame to transform the object to.\n parse: bool\n Whether to attempt extracting the coordinates from the name by\n parsing with a regex. For objects catalog names that have\n J-coordinates embedded in their names, e.g.,\n 'CRTS SSS100805 J194428-420209', this may be much faster than a\n Sesame query for the same object name. The coordinates extracted\n in this way may differ from the database coordinates by a few\n deci-arcseconds, so only use this option if you do not need\n sub-arcsecond accuracy for coordinates.\n cache : bool, optional\n Determines whether to cache the results or not. To update or\n overwrite an existing value, pass ``cache='update'``.\n\n Returns\n -------\n coord : SkyCoord\n Instance of the SkyCoord class.\n \"\"\"\n\n from .name_resolve import get_icrs_coordinates\n\n icrs_coord = get_icrs_coordinates(name, parse, cache=cache)\n icrs_sky_coord = cls(icrs_coord)\n if frame in ('icrs', icrs_coord.__class__):\n return icrs_sky_coord\n else:\n return icrs_sky_coord.transform_to(frame)\n","# Licensed under a 3-clause BSD style license - see PYFITS.rst\n\nimport gzip\nimport io\nimport mmap\nimport errno\nimport os\nimport pathlib\nimport urllib.request\nimport warnings\nimport zipfile\nfrom unittest.mock import patch\n\nimport pytest\nimport numpy as np\n\nfrom . import FitsTestCase\n\nfrom astropy.io.fits.convenience import _getext\nfrom astropy.io.fits.diff import FITSDiff\nfrom astropy.io.fits.file import _File, GZIP_MAGIC\n\nfrom astropy.io import fits\nfrom astropy.tests.helper import raises, catch_warnings, ignore_warnings\nfrom astropy.utils.data import conf\nfrom astropy.utils.exceptions import AstropyUserWarning\nfrom astropy.utils import data\n\n# NOTE: Python can be built without bz2.\ntry:\n import bz2\nexcept ImportError:\n HAS_BZ2 = False\nelse:\n HAS_BZ2 = True\n\n\nclass TestCore(FitsTestCase):\n\n @raises(OSError)\n def test_missing_file(self):\n fits.open(self.temp('does-not-exist.fits'))\n\n def test_naxisj_check(self):\n with fits.open(self.data('o4sp040b0_raw.fits')) as hdulist:\n hdulist[1].header['NAXIS3'] = 500\n\n assert 'NAXIS3' in hdulist[1].header\n hdulist.verify('silentfix')\n assert 'NAXIS3' not in hdulist[1].header\n\n def test_byteswap(self):\n p = fits.PrimaryHDU()\n l = fits.HDUList()\n\n n = np.zeros(3, dtype='i2')\n n[0] = 1\n n[1] = 60000\n n[2] = 2\n\n c = fits.Column(name='foo', format='i2', bscale=1, bzero=32768,\n array=n)\n t = fits.BinTableHDU.from_columns([c])\n\n l.append(p)\n l.append(t)\n\n l.writeto(self.temp('test.fits'), overwrite=True)\n\n with fits.open(self.temp('test.fits')) as p:\n assert p[1].data[1]['foo'] == 60000.0\n\n def test_fits_file_path_object(self):\n \"\"\"\n Testing when fits file is passed as pathlib.Path object #4412.\n \"\"\"\n fpath = pathlib.Path(self.data('tdim.fits'))\n with fits.open(fpath) as hdulist:\n assert hdulist[0].filebytes() == 2880\n assert hdulist[1].filebytes() == 5760\n\n with fits.open(self.data('tdim.fits')) as hdulist2:\n assert FITSDiff(hdulist2, hdulist).identical is True\n\n def test_fits_file_bytes_object(self):\n \"\"\"\n Testing when fits file is passed as bytes.\n \"\"\"\n with fits.open(self.data('tdim.fits').encode()) as hdulist:\n assert hdulist[0].filebytes() == 2880\n assert hdulist[1].filebytes() == 5760\n\n with fits.open(self.data('tdim.fits')) as hdulist2:\n assert FITSDiff(hdulist2, hdulist).identical is True\n\n def test_add_del_columns(self):\n p = fits.ColDefs([])\n p.add_col(fits.Column(name='FOO', format='3J'))\n p.add_col(fits.Column(name='BAR', format='1I'))\n assert p.names == ['FOO', 'BAR']\n p.del_col('FOO')\n assert p.names == ['BAR']\n\n def test_add_del_columns2(self):\n hdulist = fits.open(self.data('tb.fits'))\n table = hdulist[1]\n assert table.data.dtype.names == ('c1', 'c2', 'c3', 'c4')\n assert table.columns.names == ['c1', 'c2', 'c3', 'c4']\n table.columns.del_col('c1')\n assert table.data.dtype.names == ('c2', 'c3', 'c4')\n assert table.columns.names == ['c2', 'c3', 'c4']\n\n table.columns.del_col('c3')\n assert table.data.dtype.names == ('c2', 'c4')\n assert table.columns.names == ['c2', 'c4']\n\n table.columns.add_col(fits.Column('foo', '3J'))\n assert table.data.dtype.names == ('c2', 'c4', 'foo')\n assert table.columns.names == ['c2', 'c4', 'foo']\n\n hdulist.writeto(self.temp('test.fits'), overwrite=True)\n with ignore_warnings():\n # TODO: The warning raised by this test is actually indication of a\n # bug and should *not* be ignored. But as it is a known issue we\n # hide it for now. See\n # https://github.com/spacetelescope/PyFITS/issues/44\n with fits.open(self.temp('test.fits')) as hdulist:\n table = hdulist[1]\n assert table.data.dtype.names == ('c2', 'c4', 'foo')\n assert table.columns.names == ['c2', 'c4', 'foo']\n\n def test_update_header_card(self):\n \"\"\"A very basic test for the Header.update method--I'd like to add a\n few more cases to this at some point.\n \"\"\"\n\n header = fits.Header()\n comment = 'number of bits per data pixel'\n header['BITPIX'] = (16, comment)\n assert 'BITPIX' in header\n assert header['BITPIX'] == 16\n assert header.comments['BITPIX'] == comment\n\n header.update(BITPIX=32)\n assert header['BITPIX'] == 32\n assert header.comments['BITPIX'] == ''\n\n def test_set_card_value(self):\n \"\"\"Similar to test_update_header_card(), but tests the the\n `header['FOO'] = 'bar'` method of updating card values.\n \"\"\"\n\n header = fits.Header()\n comment = 'number of bits per data pixel'\n card = fits.Card.fromstring(f'BITPIX = 32 / {comment}')\n header.append(card)\n\n header['BITPIX'] = 32\n\n assert 'BITPIX' in header\n assert header['BITPIX'] == 32\n assert header.cards[0].keyword == 'BITPIX'\n assert header.cards[0].value == 32\n assert header.cards[0].comment == comment\n\n def test_uint(self):\n filename = self.data('o4sp040b0_raw.fits')\n with fits.open(filename, uint=False) as hdulist_f:\n with fits.open(filename, uint=True) as hdulist_i:\n assert hdulist_f[1].data.dtype == np.float32\n assert hdulist_i[1].data.dtype == np.uint16\n assert np.all(hdulist_f[1].data == hdulist_i[1].data)\n\n def test_fix_missing_card_append(self):\n hdu = fits.ImageHDU()\n errs = hdu.req_cards('TESTKW', None, None, 'foo', 'silentfix', [])\n assert len(errs) == 1\n assert 'TESTKW' in hdu.header\n assert hdu.header['TESTKW'] == 'foo'\n assert hdu.header.cards[-1].keyword == 'TESTKW'\n\n def test_fix_invalid_keyword_value(self):\n hdu = fits.ImageHDU()\n hdu.header['TESTKW'] = 'foo'\n errs = hdu.req_cards('TESTKW', None,\n lambda v: v == 'foo', 'foo', 'ignore', [])\n assert len(errs) == 0\n\n # Now try a test that will fail, and ensure that an error will be\n # raised in 'exception' mode\n errs = hdu.req_cards('TESTKW', None, lambda v: v == 'bar', 'bar',\n 'exception', [])\n assert len(errs) == 1\n assert errs[0][1] == \"'TESTKW' card has invalid value 'foo'.\"\n\n # See if fixing will work\n hdu.req_cards('TESTKW', None, lambda v: v == 'bar', 'bar', 'silentfix',\n [])\n assert hdu.header['TESTKW'] == 'bar'\n\n @raises(fits.VerifyError)\n def test_unfixable_missing_card(self):\n class TestHDU(fits.hdu.base.NonstandardExtHDU):\n def _verify(self, option='warn'):\n errs = super()._verify(option)\n hdu.req_cards('TESTKW', None, None, None, 'fix', errs)\n return errs\n\n @classmethod\n def match_header(cls, header):\n # Since creating this HDU class adds it to the registry we\n # don't want the file reader to possibly think any actual\n # HDU from a file should be handled by this class\n return False\n\n hdu = TestHDU(header=fits.Header())\n hdu.verify('fix')\n\n @raises(fits.VerifyError)\n def test_exception_on_verification_error(self):\n hdu = fits.ImageHDU()\n del hdu.header['XTENSION']\n hdu.verify('exception')\n\n def test_ignore_verification_error(self):\n hdu = fits.ImageHDU()\n # The default here would be to issue a warning; ensure that no warnings\n # or exceptions are raised\n with catch_warnings():\n warnings.simplefilter('error')\n del hdu.header['NAXIS']\n try:\n hdu.verify('ignore')\n except Exception as exc:\n self.fail('An exception occurred when the verification error '\n 'should have been ignored: {}'.format(exc))\n # Make sure the error wasn't fixed either, silently or otherwise\n assert 'NAXIS' not in hdu.header\n\n @raises(ValueError)\n def test_unrecognized_verify_option(self):\n hdu = fits.ImageHDU()\n hdu.verify('foobarbaz')\n\n def test_errlist_basic(self):\n # Just some tests to make sure that _ErrList is setup correctly.\n # No arguments\n error_list = fits.verify._ErrList()\n assert error_list == []\n # Some contents - this is not actually working, it just makes sure they\n # are kept.\n error_list = fits.verify._ErrList([1, 2, 3])\n assert error_list == [1, 2, 3]\n\n def test_combined_verify_options(self):\n \"\"\"\n Test verify options like fix+ignore.\n \"\"\"\n\n def make_invalid_hdu():\n hdu = fits.ImageHDU()\n # Add one keyword to the header that contains a fixable defect, and one\n # with an unfixable defect\n c1 = fits.Card.fromstring(\"test = ' test'\")\n c2 = fits.Card.fromstring(\"P.I. = ' Hubble'\")\n hdu.header.append(c1)\n hdu.header.append(c2)\n return hdu\n\n # silentfix+ignore should be completely silent\n hdu = make_invalid_hdu()\n with catch_warnings():\n warnings.simplefilter('error')\n try:\n hdu.verify('silentfix+ignore')\n except Exception as exc:\n self.fail('An exception occurred when the verification error '\n 'should have been ignored: {}'.format(exc))\n\n # silentfix+warn should be quiet about the fixed HDU and only warn\n # about the unfixable one\n hdu = make_invalid_hdu()\n with catch_warnings() as w:\n hdu.verify('silentfix+warn')\n assert len(w) == 4\n assert 'Illegal keyword name' in str(w[2].message)\n\n # silentfix+exception should only mention the unfixable error in the\n # exception\n hdu = make_invalid_hdu()\n with pytest.raises(fits.VerifyError, match=r'Illegal keyword name') as excinfo:\n hdu.verify('silentfix+exception')\n assert 'not upper case' not in str(excinfo.value)\n\n # fix+ignore is not too useful, but it should warn about the fixed\n # problems while saying nothing about the unfixable problems\n hdu = make_invalid_hdu()\n with catch_warnings() as w:\n hdu.verify('fix+ignore')\n assert len(w) == 4\n assert 'not upper case' in str(w[2].message)\n\n # fix+warn\n hdu = make_invalid_hdu()\n with catch_warnings() as w:\n hdu.verify('fix+warn')\n assert len(w) == 6\n assert 'not upper case' in str(w[2].message)\n assert 'Illegal keyword name' in str(w[4].message)\n\n # fix+exception\n hdu = make_invalid_hdu()\n with pytest.raises(fits.VerifyError, match=r'Illegal keyword name') as excinfo:\n hdu.verify('fix+exception')\n assert 'not upper case' in str(excinfo.value)\n\n def test_getext(self):\n \"\"\"\n Test the various different ways of specifying an extension header in\n the convenience functions.\n \"\"\"\n filename = self.data('test0.fits')\n\n hl, ext = _getext(filename, 'readonly', 1)\n assert ext == 1\n hl.close()\n\n pytest.raises(ValueError, _getext, filename, 'readonly',\n 1, 2)\n pytest.raises(ValueError, _getext, filename, 'readonly',\n (1, 2))\n pytest.raises(ValueError, _getext, filename, 'readonly',\n 'sci', 'sci')\n pytest.raises(TypeError, _getext, filename, 'readonly',\n 1, 2, 3)\n\n hl, ext = _getext(filename, 'readonly', ext=1)\n assert ext == 1\n hl.close()\n\n hl, ext = _getext(filename, 'readonly', ext=('sci', 2))\n assert ext == ('sci', 2)\n hl.close()\n\n pytest.raises(TypeError, _getext, filename, 'readonly',\n 1, ext=('sci', 2), extver=3)\n pytest.raises(TypeError, _getext, filename, 'readonly',\n ext=('sci', 2), extver=3)\n\n hl, ext = _getext(filename, 'readonly', 'sci')\n assert ext == ('sci', 1)\n hl.close()\n\n hl, ext = _getext(filename, 'readonly', 'sci', 1)\n assert ext == ('sci', 1)\n hl.close()\n\n hl, ext = _getext(filename, 'readonly', ('sci', 1))\n assert ext == ('sci', 1)\n hl.close()\n\n hl, ext = _getext(filename, 'readonly', 'sci',\n extver=1, do_not_scale_image_data=True)\n assert ext == ('sci', 1)\n hl.close()\n\n pytest.raises(TypeError, _getext, filename, 'readonly',\n 'sci', ext=1)\n pytest.raises(TypeError, _getext, filename, 'readonly',\n 'sci', 1, extver=2)\n\n hl, ext = _getext(filename, 'readonly', extname='sci')\n assert ext == ('sci', 1)\n hl.close()\n\n hl, ext = _getext(filename, 'readonly', extname='sci',\n extver=1)\n assert ext == ('sci', 1)\n hl.close()\n\n pytest.raises(TypeError, _getext, filename, 'readonly',\n extver=1)\n\n def test_extension_name_case_sensitive(self):\n \"\"\"\n Tests that setting fits.conf.extension_name_case_sensitive at\n runtime works.\n \"\"\"\n\n hdu = fits.ImageHDU()\n hdu.name = 'sCi'\n assert hdu.name == 'SCI'\n assert hdu.header['EXTNAME'] == 'SCI'\n\n with fits.conf.set_temp('extension_name_case_sensitive', True):\n hdu = fits.ImageHDU()\n hdu.name = 'sCi'\n assert hdu.name == 'sCi'\n assert hdu.header['EXTNAME'] == 'sCi'\n\n hdu.name = 'sCi'\n assert hdu.name == 'SCI'\n assert hdu.header['EXTNAME'] == 'SCI'\n\n def test_hdu_fromstring(self):\n \"\"\"\n Tests creating a fully-formed HDU object from a string containing the\n bytes of the HDU.\n \"\"\"\n infile = self.data('test0.fits')\n outfile = self.temp('test.fits')\n\n with open(infile, 'rb') as fin:\n dat = fin.read()\n\n offset = 0\n with fits.open(infile) as hdul:\n hdulen = hdul[0]._data_offset + hdul[0]._data_size\n hdu = fits.PrimaryHDU.fromstring(dat[:hdulen])\n assert isinstance(hdu, fits.PrimaryHDU)\n assert hdul[0].header == hdu.header\n assert hdu.data is None\n\n hdu.header['TEST'] = 'TEST'\n hdu.writeto(outfile)\n with fits.open(outfile) as hdul:\n assert isinstance(hdu, fits.PrimaryHDU)\n assert hdul[0].header[:-1] == hdu.header[:-1]\n assert hdul[0].header['TEST'] == 'TEST'\n assert hdu.data is None\n\n with fits.open(infile)as hdul:\n for ext_hdu in hdul[1:]:\n offset += hdulen\n hdulen = len(str(ext_hdu.header)) + ext_hdu._data_size\n hdu = fits.ImageHDU.fromstring(dat[offset:offset + hdulen])\n assert isinstance(hdu, fits.ImageHDU)\n assert ext_hdu.header == hdu.header\n assert (ext_hdu.data == hdu.data).all()\n\n def test_nonstandard_hdu(self):\n \"\"\"\n Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/157\n\n Tests that \"Nonstandard\" HDUs with SIMPLE = F are read and written\n without prepending a superfluous and unwanted standard primary HDU.\n \"\"\"\n\n data = np.arange(100, dtype=np.uint8)\n hdu = fits.PrimaryHDU(data=data)\n hdu.header['SIMPLE'] = False\n hdu.writeto(self.temp('test.fits'))\n\n info = [(0, '', 1, 'NonstandardHDU', 5, (), '', '')]\n with fits.open(self.temp('test.fits')) as hdul:\n assert hdul.info(output=False) == info\n # NonstandardHDUs just treat the data as an unspecified array of\n # bytes. The first 100 bytes should match the original data we\n # passed in...the rest should be zeros padding out the rest of the\n # FITS block\n assert (hdul[0].data[:100] == data).all()\n assert (hdul[0].data[100:] == 0).all()\n\n def test_extname(self):\n \"\"\"Test getting/setting the EXTNAME of an HDU.\"\"\"\n\n h1 = fits.PrimaryHDU()\n assert h1.name == 'PRIMARY'\n # Normally a PRIMARY HDU should not have an EXTNAME, though it should\n # have a default .name attribute\n assert 'EXTNAME' not in h1.header\n\n # The current version of the FITS standard does allow PRIMARY HDUs to\n # have an EXTNAME, however.\n h1.name = 'NOTREAL'\n assert h1.name == 'NOTREAL'\n assert h1.header.get('EXTNAME') == 'NOTREAL'\n\n # Updating the EXTNAME in the header should update the .name\n h1.header['EXTNAME'] = 'TOOREAL'\n assert h1.name == 'TOOREAL'\n\n # If we delete an EXTNAME keyword from a PRIMARY HDU it should go back\n # to the default\n del h1.header['EXTNAME']\n assert h1.name == 'PRIMARY'\n\n # For extension HDUs the situation is a bit simpler:\n h2 = fits.ImageHDU()\n assert h2.name == ''\n assert 'EXTNAME' not in h2.header\n h2.name = 'HELLO'\n assert h2.name == 'HELLO'\n assert h2.header.get('EXTNAME') == 'HELLO'\n h2.header['EXTNAME'] = 'GOODBYE'\n assert h2.name == 'GOODBYE'\n\n def test_extver_extlevel(self):\n \"\"\"Test getting/setting the EXTVER and EXTLEVEL of and HDU.\"\"\"\n\n # EXTVER and EXTNAME work exactly the same; their semantics are, for\n # now, to be inferred by the user. Although they should never be less\n # than 1, the standard does not explicitly forbid any value so long as\n # it's an integer\n h1 = fits.PrimaryHDU()\n assert h1.ver == 1\n assert h1.level == 1\n assert 'EXTVER' not in h1.header\n assert 'EXTLEVEL' not in h1.header\n\n h1.ver = 2\n assert h1.header.get('EXTVER') == 2\n h1.header['EXTVER'] = 3\n assert h1.ver == 3\n del h1.header['EXTVER']\n h1.ver == 1\n\n h1.level = 2\n assert h1.header.get('EXTLEVEL') == 2\n h1.header['EXTLEVEL'] = 3\n assert h1.level == 3\n del h1.header['EXTLEVEL']\n assert h1.level == 1\n\n pytest.raises(TypeError, setattr, h1, 'ver', 'FOO')\n pytest.raises(TypeError, setattr, h1, 'level', 'BAR')\n\n def test_consecutive_writeto(self):\n \"\"\"\n Regression test for an issue where calling writeto twice on the same\n HDUList could write a corrupted file.\n\n https://github.com/spacetelescope/PyFITS/issues/40 is actually a\n particular instance of this problem, though isn't unique to sys.stdout.\n \"\"\"\n\n with fits.open(self.data('test0.fits')) as hdul1:\n # Add a bunch of header keywords so that the data will be forced to\n # new offsets within the file:\n for idx in range(40):\n hdul1[1].header[f'TEST{idx}'] = 'test'\n\n hdul1.writeto(self.temp('test1.fits'))\n hdul1.writeto(self.temp('test2.fits'))\n\n # Open a second handle to the original file and compare it to hdul1\n # (We only compare part of the one header that was modified)\n # Compare also with the second writeto output\n with fits.open(self.data('test0.fits')) as hdul2:\n with fits.open(self.temp('test2.fits')) as hdul3:\n for hdul in (hdul1, hdul3):\n for idx, hdus in enumerate(zip(hdul2, hdul)):\n hdu2, hdu = hdus\n if idx != 1:\n assert hdu.header == hdu2.header\n else:\n assert (hdu2.header ==\n hdu.header[:len(hdu2.header)])\n assert np.all(hdu.data == hdu2.data)\n\n\nclass TestConvenienceFunctions(FitsTestCase):\n def test_writeto(self):\n \"\"\"\n Simple test for writing a trivial header and some data to a file\n with the `writeto()` convenience function.\n \"\"\"\n filename = self.temp('array.fits')\n data = np.zeros((100, 100))\n header = fits.Header()\n fits.writeto(filename, data, header=header, overwrite=True)\n with fits.open(filename) as hdul:\n assert len(hdul) == 1\n assert (data == hdul[0].data).all()\n\n def test_writeto_2(self):\n \"\"\"\n Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/107\n\n Test of `writeto()` with a trivial header containing a single keyword.\n \"\"\"\n filename = self.temp('array.fits')\n data = np.zeros((100, 100))\n header = fits.Header()\n header.set('CRPIX1', 1.)\n fits.writeto(filename, data, header=header,\n overwrite=True, output_verify='silentfix')\n with fits.open(filename) as hdul:\n assert len(hdul) == 1\n assert (data == hdul[0].data).all()\n assert 'CRPIX1' in hdul[0].header\n assert hdul[0].header['CRPIX1'] == 1.0\n\n\nclass TestFileFunctions(FitsTestCase):\n \"\"\"\n Tests various basic I/O operations, specifically in the\n astropy.io.fits.file._File class.\n \"\"\"\n\n def test_open_nonexistent(self):\n \"\"\"Test that trying to open a non-existent file results in an\n OSError (and not some other arbitrary exception).\n \"\"\"\n\n with pytest.raises(OSError, match=r'No such file or directory'):\n fits.open(self.temp('foobar.fits'))\n\n # But opening in ostream or append mode should be okay, since they\n # allow writing new files\n for mode in ('ostream', 'append'):\n with fits.open(self.temp('foobar.fits'), mode=mode) as _:\n pass\n\n assert os.path.exists(self.temp('foobar.fits'))\n os.remove(self.temp('foobar.fits'))\n\n def test_open_file_handle(self):\n # Make sure we can open a FITS file from an open file handle\n with open(self.data('test0.fits'), 'rb') as handle:\n with fits.open(handle) as _:\n pass\n\n with open(self.temp('temp.fits'), 'wb') as handle:\n with fits.open(handle, mode='ostream') as _:\n pass\n\n # Opening without explicitly specifying binary mode should fail\n with pytest.raises(ValueError):\n with open(self.data('test0.fits')) as handle:\n with fits.open(handle) as _:\n pass\n\n # All of these read modes should fail\n for mode in ['r', 'rt']:\n with pytest.raises(ValueError):\n with open(self.data('test0.fits'), mode=mode) as handle:\n with fits.open(handle) as _:\n pass\n\n # These update or write modes should fail as well\n for mode in ['w', 'wt', 'w+', 'wt+', 'r+', 'rt+',\n 'a', 'at', 'a+', 'at+']:\n with pytest.raises(ValueError):\n with open(self.temp('temp.fits'), mode=mode) as handle:\n with fits.open(handle) as _:\n pass\n\n def test_fits_file_handle_mode_combo(self):\n # This should work fine since no mode is given\n with open(self.data('test0.fits'), 'rb') as handle:\n with fits.open(handle) as _:\n pass\n\n # This should work fine since the modes are compatible\n with open(self.data('test0.fits'), 'rb') as handle:\n with fits.open(handle, mode='readonly') as _:\n pass\n\n # This should not work since the modes conflict\n with pytest.raises(ValueError):\n with open(self.data('test0.fits'), 'rb') as handle:\n with fits.open(handle, mode='ostream') as _:\n pass\n\n def test_open_from_url(self):\n file_url = 'file:///' + self.data('test0.fits').lstrip('/')\n with urllib.request.urlopen(file_url) as urlobj:\n with fits.open(urlobj) as _:\n pass\n\n # It will not be possible to write to a file that is from a URL object\n for mode in ('ostream', 'append', 'update'):\n with pytest.raises(ValueError):\n with urllib.request.urlopen(file_url) as urlobj:\n with fits.open(urlobj, mode=mode) as _:\n pass\n\n @pytest.mark.remote_data(source='astropy')\n def test_open_from_remote_url(self):\n for dataurl in (conf.dataurl, conf.dataurl_mirror):\n remote_url = '{}/{}'.format(dataurl, 'allsky/allsky_rosat.fits')\n try:\n with urllib.request.urlopen(remote_url) as urlobj:\n with fits.open(urlobj) as fits_handle:\n assert len(fits_handle) == 1\n\n for mode in ('ostream', 'append', 'update'):\n with pytest.raises(ValueError):\n with urllib.request.urlopen(remote_url) as urlobj:\n with fits.open(urlobj, mode=mode) as fits_handle:\n assert len(fits_handle) == 1\n except (urllib.error.HTTPError, urllib.error.URLError):\n continue\n else:\n break\n else:\n raise Exception(\"Could not download file\")\n\n def test_open_gzipped(self):\n gzip_file = self._make_gzip_file()\n with ignore_warnings():\n with fits.open(gzip_file) as fits_handle:\n assert fits_handle._file.compression == 'gzip'\n assert len(fits_handle) == 5\n with fits.open(gzip.GzipFile(gzip_file)) as fits_handle:\n assert fits_handle._file.compression == 'gzip'\n assert len(fits_handle) == 5\n\n def test_open_gzipped_from_handle(self):\n with open(self._make_gzip_file(), 'rb') as handle:\n with fits.open(handle) as fits_handle:\n assert fits_handle._file.compression == 'gzip'\n\n def test_detect_gzipped(self):\n \"\"\"Test detection of a gzip file when the extension is not .gz.\"\"\"\n with ignore_warnings():\n with fits.open(self._make_gzip_file('test0.fz')) as fits_handle:\n assert fits_handle._file.compression == 'gzip'\n assert len(fits_handle) == 5\n\n def test_writeto_append_mode_gzip(self):\n \"\"\"Regression test for\n https://github.com/spacetelescope/PyFITS/issues/33\n\n Check that a new GzipFile opened in append mode can be used to write\n out a new FITS file.\n \"\"\"\n\n # Note: when opening a GzipFile the 'b+' is superfluous, but this was\n # still how the original test case looked\n # Note: with statement not supported on GzipFile in older Python\n # versions\n fileobj = gzip.GzipFile(self.temp('test.fits.gz'), 'ab+')\n h = fits.PrimaryHDU()\n try:\n h.writeto(fileobj)\n finally:\n fileobj.close()\n\n with fits.open(self.temp('test.fits.gz')) as hdul:\n assert hdul[0].header == h.header\n\n def test_fits_update_mode_gzip(self):\n \"\"\"Test updating a GZipped FITS file\"\"\"\n\n with fits.open(self._make_gzip_file('update.gz'), mode='update') as fits_handle:\n hdu = fits.ImageHDU(data=[x for x in range(100)])\n fits_handle.append(hdu)\n\n with fits.open(self.temp('update.gz')) as new_handle:\n assert len(new_handle) == 6\n assert (new_handle[-1].data == [x for x in range(100)]).all()\n\n def test_fits_append_mode_gzip(self):\n \"\"\"Make sure that attempting to open an existing GZipped FITS file in\n 'append' mode raises an error\"\"\"\n\n with pytest.raises(OSError):\n with fits.open(self._make_gzip_file('append.gz'), mode='append') as _:\n pass\n\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\n def test_open_bzipped(self):\n bzip_file = self._make_bzip2_file()\n with ignore_warnings():\n with fits.open(bzip_file) as fits_handle:\n assert fits_handle._file.compression == 'bzip2'\n assert len(fits_handle) == 5\n\n with fits.open(bz2.BZ2File(bzip_file)) as fits_handle:\n assert fits_handle._file.compression == 'bzip2'\n assert len(fits_handle) == 5\n\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\n def test_open_bzipped_from_handle(self):\n with open(self._make_bzip2_file(), 'rb') as handle:\n with fits.open(handle) as fits_handle:\n assert fits_handle._file.compression == 'bzip2'\n assert len(fits_handle) == 5\n\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\n def test_detect_bzipped(self):\n \"\"\"Test detection of a bzip2 file when the extension is not .bz2.\"\"\"\n with ignore_warnings():\n with fits.open(self._make_bzip2_file('test0.xx')) as fits_handle:\n assert fits_handle._file.compression == 'bzip2'\n assert len(fits_handle) == 5\n\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\n def test_writeto_bzip2_fileobj(self):\n \"\"\"Test writing to a bz2.BZ2File file like object\"\"\"\n fileobj = bz2.BZ2File(self.temp('test.fits.bz2'), 'w')\n h = fits.PrimaryHDU()\n try:\n h.writeto(fileobj)\n finally:\n fileobj.close()\n\n with fits.open(self.temp('test.fits.bz2')) as hdul:\n assert hdul[0].header == h.header\n\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\n def test_writeto_bzip2_filename(self):\n \"\"\"Test writing to a bzip2 file by name\"\"\"\n filename = self.temp('testname.fits.bz2')\n h = fits.PrimaryHDU()\n h.writeto(filename)\n\n with fits.open(self.temp('testname.fits.bz2')) as hdul:\n assert hdul[0].header == h.header\n\n def test_open_zipped(self):\n zip_file = self._make_zip_file()\n with ignore_warnings():\n with fits.open(zip_file) as fits_handle:\n assert fits_handle._file.compression == 'zip'\n assert len(fits_handle) == 5\n with fits.open(zipfile.ZipFile(zip_file)) as fits_handle:\n assert fits_handle._file.compression == 'zip'\n assert len(fits_handle) == 5\n\n def test_open_zipped_from_handle(self):\n with open(self._make_zip_file(), 'rb') as handle:\n with fits.open(handle) as fits_handle:\n assert fits_handle._file.compression == 'zip'\n assert len(fits_handle) == 5\n\n def test_detect_zipped(self):\n \"\"\"Test detection of a zip file when the extension is not .zip.\"\"\"\n\n zf = self._make_zip_file(filename='test0.fz')\n with ignore_warnings():\n assert len(fits.open(zf)) == 5\n\n def test_open_zipped_writeable(self):\n \"\"\"Opening zipped files in a writeable mode should fail.\"\"\"\n\n zf = self._make_zip_file()\n pytest.raises(OSError, fits.open, zf, 'update')\n pytest.raises(OSError, fits.open, zf, 'append')\n\n zf = zipfile.ZipFile(zf, 'a')\n pytest.raises(OSError, fits.open, zf, 'update')\n pytest.raises(OSError, fits.open, zf, 'append')\n\n def test_read_open_astropy_gzip_file(self):\n \"\"\"\n Regression test for https://github.com/astropy/astropy/issues/2774\n\n This tests reading from a ``GzipFile`` object from Astropy's\n compatibility copy of the ``gzip`` module.\n \"\"\"\n gf = gzip.GzipFile(self._make_gzip_file())\n try:\n assert len(fits.open(gf)) == 5\n finally:\n gf.close()\n\n @raises(OSError)\n def test_open_multiple_member_zipfile(self):\n \"\"\"\n Opening zip files containing more than one member files should fail\n as there's no obvious way to specify which file is the FITS file to\n read.\n \"\"\"\n\n zfile = zipfile.ZipFile(self.temp('test0.zip'), 'w')\n zfile.write(self.data('test0.fits'))\n zfile.writestr('foo', 'bar')\n zfile.close()\n\n fits.open(zfile.filename)\n\n def test_read_open_file(self):\n \"\"\"Read from an existing file object.\"\"\"\n\n with open(self.data('test0.fits'), 'rb') as f:\n assert len(fits.open(f)) == 5\n\n def test_read_closed_file(self):\n \"\"\"Read from an existing file object that's been closed.\"\"\"\n\n f = open(self.data('test0.fits'), 'rb')\n f.close()\n with fits.open(f) as f2:\n assert len(f2) == 5\n\n def test_read_open_gzip_file(self):\n \"\"\"Read from an open gzip file object.\"\"\"\n\n gf = gzip.GzipFile(self._make_gzip_file())\n try:\n assert len(fits.open(gf)) == 5\n finally:\n gf.close()\n\n def test_open_gzip_file_for_writing(self):\n \"\"\"Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/195.\"\"\"\n\n gf = self._make_gzip_file()\n with fits.open(gf, mode='update') as h:\n h[0].header['EXPFLAG'] = 'ABNORMAL'\n h[1].data[0, 0] = 1\n with fits.open(gf) as h:\n # Just to make sur ethe update worked; if updates work\n # normal writes should work too...\n assert h[0].header['EXPFLAG'] == 'ABNORMAL'\n assert h[1].data[0, 0] == 1\n\n def test_write_read_gzip_file(self):\n \"\"\"\n Regression test for https://github.com/astropy/astropy/issues/2794\n\n Ensure files written through gzip are readable.\n \"\"\"\n\n data = np.arange(100)\n hdu = fits.PrimaryHDU(data=data)\n hdu.writeto(self.temp('test.fits.gz'))\n\n with open(self.temp('test.fits.gz'), 'rb') as f:\n assert f.read(3) == GZIP_MAGIC\n\n with fits.open(self.temp('test.fits.gz')) as hdul:\n assert np.all(hdul[0].data == data)\n\n def test_read_file_like_object(self):\n \"\"\"Test reading a FITS file from a file-like object.\"\"\"\n\n filelike = io.BytesIO()\n with open(self.data('test0.fits'), 'rb') as f:\n filelike.write(f.read())\n filelike.seek(0)\n with ignore_warnings():\n assert len(fits.open(filelike)) == 5\n\n def test_updated_file_permissions(self):\n \"\"\"\n Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/79\n\n Tests that when a FITS file is modified in update mode, the file\n permissions are preserved.\n \"\"\"\n\n filename = self.temp('test.fits')\n hdul = [fits.PrimaryHDU(), fits.ImageHDU()]\n hdul = fits.HDUList(hdul)\n hdul.writeto(filename)\n\n old_mode = os.stat(filename).st_mode\n\n hdul = fits.open(filename, mode='update')\n hdul.insert(1, fits.ImageHDU())\n hdul.flush()\n hdul.close()\n\n assert old_mode == os.stat(filename).st_mode\n\n def test_fileobj_mode_guessing(self):\n \"\"\"Tests whether a file opened without a specified io.fits mode\n ('readonly', etc.) is opened in a mode appropriate for the given file\n object.\n \"\"\"\n\n self.copy_file('test0.fits')\n\n # Opening in text mode should outright fail\n for mode in ('r', 'w', 'a'):\n with open(self.temp('test0.fits'), mode) as f:\n pytest.raises(ValueError, fits.HDUList.fromfile, f)\n\n # Need to re-copy the file since opening it in 'w' mode blew it away\n self.copy_file('test0.fits')\n\n with open(self.temp('test0.fits'), 'rb') as f:\n with fits.HDUList.fromfile(f) as h:\n assert h.fileinfo(0)['filemode'] == 'readonly'\n\n for mode in ('wb', 'ab'):\n with open(self.temp('test0.fits'), mode) as f:\n with fits.HDUList.fromfile(f) as h:\n # Basically opening empty files for output streaming\n assert len(h) == 0\n\n # Need to re-copy the file since opening it in 'w' mode blew it away\n self.copy_file('test0.fits')\n\n with open(self.temp('test0.fits'), 'wb+') as f:\n with fits.HDUList.fromfile(f) as h:\n # wb+ still causes an existing file to be overwritten so there\n # are no HDUs\n assert len(h) == 0\n\n # Need to re-copy the file since opening it in 'w' mode blew it away\n self.copy_file('test0.fits')\n\n with open(self.temp('test0.fits'), 'rb+') as f:\n with fits.HDUList.fromfile(f) as h:\n assert h.fileinfo(0)['filemode'] == 'update'\n\n with open(self.temp('test0.fits'), 'ab+') as f:\n with fits.HDUList.fromfile(f) as h:\n assert h.fileinfo(0)['filemode'] == 'append'\n\n def test_mmap_unwriteable(self):\n \"\"\"Regression test for https://github.com/astropy/astropy/issues/968\n\n Temporarily patches mmap.mmap to exhibit platform-specific bad\n behavior.\n \"\"\"\n\n class MockMmap(mmap.mmap):\n def flush(self):\n raise OSError('flush is broken on this platform')\n\n old_mmap = mmap.mmap\n mmap.mmap = MockMmap\n\n # Force the mmap test to be rerun\n _File.__dict__['_mmap_available']._cache.clear()\n\n try:\n self.copy_file('test0.fits')\n with catch_warnings() as w:\n with fits.open(self.temp('test0.fits'), mode='update',\n memmap=True) as h:\n h[1].data[0, 0] = 999\n\n assert len(w) == 1\n assert 'mmap.flush is unavailable' in str(w[0].message)\n\n # Double check that writing without mmap still worked\n with fits.open(self.temp('test0.fits')) as h:\n assert h[1].data[0, 0] == 999\n finally:\n mmap.mmap = old_mmap\n _File.__dict__['_mmap_available']._cache.clear()\n\n @pytest.mark.openfiles_ignore\n def test_mmap_allocate_error(self):\n \"\"\"\n Regression test for https://github.com/astropy/astropy/issues/1380\n\n Temporarily patches mmap.mmap to raise an OSError if mode is ACCESS_COPY.\n \"\"\"\n\n mmap_original = mmap.mmap\n\n # We patch mmap here to raise an error if access=mmap.ACCESS_COPY, which\n # emulates an issue that an OSError is raised if the available address\n # space is less than the size of the file even if memory mapping is used.\n\n def mmap_patched(*args, **kwargs):\n if kwargs.get('access') == mmap.ACCESS_COPY:\n exc = OSError()\n exc.errno = errno.ENOMEM\n raise exc\n else:\n return mmap_original(*args, **kwargs)\n\n with fits.open(self.data('test0.fits'), memmap=True) as hdulist:\n with patch.object(mmap, 'mmap', side_effect=mmap_patched) as p:\n with pytest.warns(AstropyUserWarning, match=r\"Could not memory \"\n r\"map array with mode='readonly'\"):\n data = hdulist[1].data\n p.reset_mock()\n assert not data.flags.writeable\n\n def test_mmap_closing(self):\n \"\"\"\n Tests that the mmap reference is closed/removed when there aren't any\n HDU data references left.\n \"\"\"\n\n if not _File._mmap_available:\n pytest.xfail('not expected to work on platforms without mmap '\n 'support')\n\n with fits.open(self.data('test0.fits'), memmap=True) as hdul:\n assert hdul._file._mmap is None\n\n hdul[1].data\n assert hdul._file._mmap is not None\n\n del hdul[1].data\n # Should be no more references to data in the file so close the\n # mmap\n assert hdul._file._mmap is None\n\n hdul[1].data\n hdul[2].data\n del hdul[1].data\n # hdul[2].data is still references so keep the mmap open\n assert hdul._file._mmap is not None\n del hdul[2].data\n assert hdul._file._mmap is None\n\n assert hdul._file._mmap is None\n\n with fits.open(self.data('test0.fits'), memmap=True) as hdul:\n hdul[1].data\n\n # When the only reference to the data is on the hdu object, and the\n # hdulist it belongs to has been closed, the mmap should be closed as\n # well\n assert hdul._file._mmap is None\n\n with fits.open(self.data('test0.fits'), memmap=True) as hdul:\n data = hdul[1].data\n # also make a copy\n data_copy = data.copy()\n\n # The HDUList is closed; in fact, get rid of it completely\n del hdul\n\n # The data array should still work though...\n assert np.all(data == data_copy)\n\n def test_uncloseable_file(self):\n \"\"\"\n Regression test for https://github.com/astropy/astropy/issues/2356\n\n Demonstrates that FITS files can still be read from \"file-like\" objects\n that don't have an obvious \"open\" or \"closed\" state.\n \"\"\"\n\n class MyFileLike:\n def __init__(self, foobar):\n self._foobar = foobar\n\n def read(self, n):\n return self._foobar.read(n)\n\n def seek(self, offset, whence=os.SEEK_SET):\n self._foobar.seek(offset, whence)\n\n def tell(self):\n return self._foobar.tell()\n\n with open(self.data('test0.fits'), 'rb') as f:\n fileobj = MyFileLike(f)\n\n with fits.open(fileobj) as hdul1:\n with fits.open(self.data('test0.fits')) as hdul2:\n assert hdul1.info(output=False) == hdul2.info(output=False)\n for hdu1, hdu2 in zip(hdul1, hdul2):\n assert hdu1.header == hdu2.header\n if hdu1.data is not None and hdu2.data is not None:\n assert np.all(hdu1.data == hdu2.data)\n\n def test_write_bytesio_discontiguous(self):\n \"\"\"\n Regression test related to\n https://github.com/astropy/astropy/issues/2794#issuecomment-55441539\n\n Demonstrates that writing an HDU containing a discontiguous Numpy array\n should work properly.\n \"\"\"\n\n data = np.arange(100)[::3]\n hdu = fits.PrimaryHDU(data=data)\n fileobj = io.BytesIO()\n hdu.writeto(fileobj)\n\n fileobj.seek(0)\n\n with fits.open(fileobj) as h:\n assert np.all(h[0].data == data)\n\n def test_write_bytesio(self):\n \"\"\"\n Regression test for https://github.com/astropy/astropy/issues/2463\n\n Test againt `io.BytesIO`. `io.StringIO` is not supported.\n \"\"\"\n\n self._test_write_string_bytes_io(io.BytesIO())\n\n @pytest.mark.skipif('sys.platform.startswith(\"win32\")')\n def test_filename_with_colon(self):\n \"\"\"\n Test reading and writing a file with a colon in the filename.\n\n Regression test for https://github.com/astropy/astropy/issues/3122\n \"\"\"\n\n # Skip on Windows since colons in filenames makes NTFS sad.\n\n filename = 'APEXHET.2014-04-01T15:18:01.000.fits'\n hdu = fits.PrimaryHDU(data=np.arange(10))\n hdu.writeto(self.temp(filename))\n\n with fits.open(self.temp(filename)) as hdul:\n assert np.all(hdul[0].data == hdu.data)\n\n def test_writeto_full_disk(self, monkeypatch):\n \"\"\"\n Test that it gives a readable error when trying to write an hdulist\n to a full disk.\n \"\"\"\n\n def _writeto(self, array):\n raise OSError(\"Fake error raised when writing file.\")\n\n def get_free_space_in_dir(path):\n return 0\n\n with pytest.raises(OSError) as exc:\n monkeypatch.setattr(fits.hdu.base._BaseHDU, \"_writeto\", _writeto)\n monkeypatch.setattr(data, \"get_free_space_in_dir\", get_free_space_in_dir)\n\n n = np.arange(0, 1000, dtype='int64')\n hdu = fits.PrimaryHDU(n)\n hdulist = fits.HDUList(hdu)\n filename = self.temp('test.fits')\n\n with open(filename, mode='wb') as fileobj:\n hdulist.writeto(fileobj)\n\n assert (\"Not enough space on disk: requested 8000, available 0. \"\n \"Fake error raised when writing file.\") == exc.value.args[0]\n\n def test_flush_full_disk(self, monkeypatch):\n \"\"\"\n Test that it gives a readable error when trying to update an hdulist\n to a full disk.\n \"\"\"\n filename = self.temp('test.fits')\n hdul = [fits.PrimaryHDU(), fits.ImageHDU()]\n hdul = fits.HDUList(hdul)\n hdul[0].data = np.arange(0, 1000, dtype='int64')\n hdul.writeto(filename)\n\n def _writedata(self, fileobj):\n raise OSError(\"Fake error raised when writing file.\")\n\n def get_free_space_in_dir(path):\n return 0\n\n monkeypatch.setattr(fits.hdu.base._BaseHDU, \"_writedata\", _writedata)\n monkeypatch.setattr(data, \"get_free_space_in_dir\",\n get_free_space_in_dir)\n\n with pytest.raises(OSError) as exc:\n with fits.open(filename, mode='update') as hdul:\n hdul[0].data = np.arange(0, 1000, dtype='int64')\n hdul.insert(1, fits.ImageHDU())\n hdul.flush()\n\n assert (\"Not enough space on disk: requested 8000, available 0. \"\n \"Fake error raised when writing file.\") == exc.value.args[0]\n\n def _test_write_string_bytes_io(self, fileobj):\n \"\"\"\n Implemented for both test_write_stringio and test_write_bytesio.\n \"\"\"\n\n with fits.open(self.data('test0.fits')) as hdul:\n hdul.writeto(fileobj)\n hdul2 = fits.HDUList.fromstring(fileobj.getvalue())\n assert FITSDiff(hdul, hdul2).identical\n\n def _make_gzip_file(self, filename='test0.fits.gz'):\n gzfile = self.temp(filename)\n with open(self.data('test0.fits'), 'rb') as f:\n gz = gzip.open(gzfile, 'wb')\n gz.write(f.read())\n gz.close()\n\n return gzfile\n\n def _make_zip_file(self, mode='copyonwrite', filename='test0.fits.zip'):\n zfile = zipfile.ZipFile(self.temp(filename), 'w')\n zfile.write(self.data('test0.fits'))\n zfile.close()\n\n return zfile.filename\n\n def _make_bzip2_file(self, filename='test0.fits.bz2'):\n bzfile = self.temp(filename)\n with open(self.data('test0.fits'), 'rb') as f:\n bz = bz2.BZ2File(bzfile, 'w')\n bz.write(f.read())\n bz.close()\n\n return bzfile\n\n def test_simulateonly(self):\n \"\"\"Write to None simulates writing.\"\"\"\n\n with fits.open(self.data('test0.fits')) as hdul:\n hdul.writeto(None)\n hdul[0].writeto(None)\n hdul[0].header.tofile(None)\n\n\nclass TestStreamingFunctions(FitsTestCase):\n \"\"\"Test functionality of the StreamingHDU class.\"\"\"\n\n def test_streaming_hdu(self):\n shdu = self._make_streaming_hdu(self.temp('new.fits'))\n assert isinstance(shdu.size, int)\n assert shdu.size == 100\n shdu.close()\n\n @raises(ValueError)\n def test_streaming_hdu_file_wrong_mode(self):\n \"\"\"\n Test that streaming an HDU to a file opened in the wrong mode fails as\n expected.\n \"\"\"\n\n with open(self.temp('new.fits'), 'wb') as f:\n header = fits.Header()\n fits.StreamingHDU(f, header)\n\n def test_streaming_hdu_write_file(self):\n \"\"\"Test streaming an HDU to an open file object.\"\"\"\n\n arr = np.zeros((5, 5), dtype=np.int32)\n with open(self.temp('new.fits'), 'ab+') as f:\n shdu = self._make_streaming_hdu(f)\n shdu.write(arr)\n assert shdu.writecomplete\n assert shdu.size == 100\n with fits.open(self.temp('new.fits')) as hdul:\n assert len(hdul) == 1\n assert (hdul[0].data == arr).all()\n\n def test_streaming_hdu_write_file_like(self):\n \"\"\"Test streaming an HDU to an open file-like object.\"\"\"\n\n arr = np.zeros((5, 5), dtype=np.int32)\n # The file-like object underlying a StreamingHDU must be in binary mode\n sf = io.BytesIO()\n shdu = self._make_streaming_hdu(sf)\n shdu.write(arr)\n assert shdu.writecomplete\n assert shdu.size == 100\n\n sf.seek(0)\n hdul = fits.open(sf)\n assert len(hdul) == 1\n assert (hdul[0].data == arr).all()\n\n def test_streaming_hdu_append_extension(self):\n arr = np.zeros((5, 5), dtype=np.int32)\n with open(self.temp('new.fits'), 'ab+') as f:\n shdu = self._make_streaming_hdu(f)\n shdu.write(arr)\n # Doing this again should update the file with an extension\n with open(self.temp('new.fits'), 'ab+') as f:\n shdu = self._make_streaming_hdu(f)\n shdu.write(arr)\n\n def test_fix_invalid_extname(self, capsys):\n phdu = fits.PrimaryHDU()\n ihdu = fits.ImageHDU()\n ihdu.header['EXTNAME'] = 12345678\n hdul = fits.HDUList([phdu, ihdu])\n filename = self.temp('temp.fits')\n\n pytest.raises(fits.VerifyError, hdul.writeto, filename,\n output_verify='exception')\n with pytest.warns(fits.verify.VerifyWarning,\n match=r'Verification reported errors'):\n hdul.writeto(filename, output_verify='fix')\n with fits.open(filename):\n assert hdul[1].name == '12345678'\n assert hdul[1].header['EXTNAME'] == '12345678'\n\n hdul.close()\n\n def _make_streaming_hdu(self, fileobj):\n hd = fits.Header()\n hd['SIMPLE'] = (True, 'conforms to FITS standard')\n hd['BITPIX'] = (32, 'array data type')\n hd['NAXIS'] = (2, 'number of array dimensions')\n hd['NAXIS1'] = 5\n hd['NAXIS2'] = 5\n hd['EXTEND'] = True\n return fits.StreamingHDU(fileobj, hd)\n\n def test_blank_ignore(self):\n\n with fits.open(self.data('blank.fits'), ignore_blank=True) as f:\n assert f[0].data.flat[0] == 2\n\n def test_error_if_memmap_impossible(self):\n pth = self.data('blank.fits')\n with fits.open(pth, memmap=True) as hdul:\n with pytest.raises(ValueError):\n hdul[0].data\n\n # However, it should not fail if do_not_scale_image_data was used:\n # See https://github.com/astropy/astropy/issues/3766\n with fits.open(pth, memmap=True, do_not_scale_image_data=True) as hdul:\n hdul[0].data # Just make sure it doesn't crash\n"],"string":"[\n \"import re\\nimport copy\\nimport warnings\\nimport operator\\n\\nimport numpy as np\\n\\nfrom astropy import _erfa as erfa\\nfrom astropy.utils.compat.misc import override__dir__\\nfrom astropy import units as u\\nfrom astropy.constants import c as speed_of_light\\nfrom astropy.utils.data_info import MixinInfo\\nfrom astropy.utils import ShapedLikeNDArray\\nfrom astropy.time import Time\\nfrom astropy.utils.exceptions import AstropyUserWarning\\n\\nfrom .distances import Distance\\nfrom .angles import Angle\\nfrom .baseframe import (BaseCoordinateFrame, frame_transform_graph,\\n GenericFrame)\\nfrom .builtin_frames import ICRS, SkyOffsetFrame\\nfrom .representation import (SphericalRepresentation,\\n UnitSphericalRepresentation, SphericalDifferential)\\nfrom .sky_coordinate_parsers import (_get_frame_class, _get_frame_without_data,\\n _parse_coordinate_data)\\n\\n__all__ = ['SkyCoord', 'SkyCoordInfo']\\n\\n\\nclass SkyCoordInfo(MixinInfo):\\n \\\"\\\"\\\"\\n Container for meta information like name, description, format. This is\\n required when the object is used as a mixin column within a table, but can\\n be used as a general way to store meta information.\\n \\\"\\\"\\\"\\n attrs_from_parent = set(['unit']) # Unit is read-only\\n _supports_indexing = False\\n\\n @staticmethod\\n def default_format(val):\\n repr_data = val.info._repr_data\\n formats = ['{0.' + compname + '.value:}' for compname\\n in repr_data.components]\\n return ','.join(formats).format(repr_data)\\n\\n @property\\n def unit(self):\\n repr_data = self._repr_data\\n unit = ','.join(str(getattr(repr_data, comp).unit) or 'None'\\n for comp in repr_data.components)\\n return unit\\n\\n @property\\n def _repr_data(self):\\n if self._parent is None:\\n return None\\n\\n sc = self._parent\\n if (issubclass(sc.representation_type, SphericalRepresentation)\\n and isinstance(sc.data, UnitSphericalRepresentation)):\\n repr_data = sc.represent_as(sc.data.__class__, in_frame_units=True)\\n else:\\n repr_data = sc.represent_as(sc.representation_type,\\n in_frame_units=True)\\n return repr_data\\n\\n def _represent_as_dict(self):\\n obj = self._parent\\n attrs = (list(obj.representation_component_names) +\\n list(frame_transform_graph.frame_attributes.keys()))\\n\\n # Don't output distance if it is all unitless 1.0\\n if 'distance' in attrs and np.all(obj.distance == 1.0):\\n attrs.remove('distance')\\n\\n out = super()._represent_as_dict(attrs)\\n\\n out['representation_type'] = obj.representation_type.get_name()\\n out['frame'] = obj.frame.name\\n # Note that obj.info.unit is a fake composite unit (e.g. 'deg,deg,None'\\n # or None,None,m) and is not stored. The individual attributes have\\n # units.\\n\\n return out\\n\\n def new_like(self, skycoords, length, metadata_conflicts='warn', name=None):\\n \\\"\\\"\\\"\\n Return a new SkyCoord instance which is consistent with the input\\n SkyCoord objects ``skycoords`` and has ``length`` rows. Being\\n \\\"consistent\\\" is defined as being able to set an item from one to each of\\n the rest without any exception being raised.\\n\\n This is intended for creating a new SkyCoord instance whose elements can\\n be set in-place for table operations like join or vstack. This is used\\n when a SkyCoord object is used as a mixin column in an astropy Table.\\n\\n The data values are not predictable and it is expected that the consumer\\n of the object will fill in all values.\\n\\n Parameters\\n ----------\\n skycoords : list\\n List of input SkyCoord objects\\n length : int\\n Length of the output skycoord object\\n metadata_conflicts : str ('warn'|'error'|'silent')\\n How to handle metadata conflicts\\n name : str\\n Output name (sets output skycoord.info.name)\\n\\n Returns\\n -------\\n skycoord : SkyCoord (or subclass)\\n Instance of this class consistent with ``skycoords``\\n\\n \\\"\\\"\\\"\\n # Get merged info attributes like shape, dtype, format, description, etc.\\n attrs = self.merge_cols_attributes(skycoords, metadata_conflicts, name,\\n ('meta', 'description'))\\n skycoord0 = skycoords[0]\\n\\n # Make a new SkyCoord object with the desired length and attributes\\n # by using the _apply / __getitem__ machinery to effectively return\\n # skycoord0[[0, 0, ..., 0, 0]]. This will have the all the right frame\\n # attributes with the right shape.\\n indexes = np.zeros(length, dtype=np.int64)\\n out = skycoord0[indexes]\\n\\n # Use __setitem__ machinery to check for consistency of all skycoords\\n for skycoord in skycoords[1:]:\\n try:\\n out[0] = skycoord[0]\\n except Exception as err:\\n raise ValueError(f'input skycoords are inconsistent: {err}')\\n\\n # Set (merged) info attributes\\n for attr in ('name', 'meta', 'description'):\\n if attr in attrs:\\n setattr(out.info, attr, attrs[attr])\\n\\n return out\\n\\n\\nclass SkyCoord(ShapedLikeNDArray):\\n \\\"\\\"\\\"High-level object providing a flexible interface for celestial coordinate\\n representation, manipulation, and transformation between systems.\\n\\n The `SkyCoord` class accepts a wide variety of inputs for initialization. At\\n a minimum these must provide one or more celestial coordinate values with\\n unambiguous units. Inputs may be scalars or lists/tuples/arrays, yielding\\n scalar or array coordinates (can be checked via ``SkyCoord.isscalar``).\\n Typically one also specifies the coordinate frame, though this is not\\n required. The general pattern for spherical representations is::\\n\\n SkyCoord(COORD, [FRAME], keyword_args ...)\\n SkyCoord(LON, LAT, [FRAME], keyword_args ...)\\n SkyCoord(LON, LAT, [DISTANCE], frame=FRAME, unit=UNIT, keyword_args ...)\\n SkyCoord([FRAME], <lon_attr>=LON, <lat_attr>=LAT, keyword_args ...)\\n\\n It is also possible to input coordinate values in other representations\\n such as cartesian or cylindrical. In this case one includes the keyword\\n argument ``representation_type='cartesian'`` (for example) along with data\\n in ``x``, ``y``, and ``z``.\\n\\n See also: http://docs.astropy.org/en/stable/coordinates/\\n\\n Examples\\n --------\\n The examples below illustrate common ways of initializing a `SkyCoord`\\n object. For a complete description of the allowed syntax see the\\n full coordinates documentation. First some imports::\\n\\n >>> from astropy.coordinates import SkyCoord # High-level coordinates\\n >>> from astropy.coordinates import ICRS, Galactic, FK4, FK5 # Low-level frames\\n >>> from astropy.coordinates import Angle, Latitude, Longitude # Angles\\n >>> import astropy.units as u\\n\\n The coordinate values and frame specification can now be provided using\\n positional and keyword arguments::\\n\\n >>> c = SkyCoord(10, 20, unit=\\\"deg\\\") # defaults to ICRS frame\\n >>> c = SkyCoord([1, 2, 3], [-30, 45, 8], frame=\\\"icrs\\\", unit=\\\"deg\\\") # 3 coords\\n\\n >>> coords = [\\\"1:12:43.2 +31:12:43\\\", \\\"1 12 43.2 +31 12 43\\\"]\\n >>> c = SkyCoord(coords, frame=FK4, unit=(u.hourangle, u.deg), obstime=\\\"J1992.21\\\")\\n\\n >>> c = SkyCoord(\\\"1h12m43.2s +1d12m43s\\\", frame=Galactic) # Units from string\\n >>> c = SkyCoord(frame=\\\"galactic\\\", l=\\\"1h12m43.2s\\\", b=\\\"+1d12m43s\\\")\\n\\n >>> ra = Longitude([1, 2, 3], unit=u.deg) # Could also use Angle\\n >>> dec = np.array([4.5, 5.2, 6.3]) * u.deg # Astropy Quantity\\n >>> c = SkyCoord(ra, dec, frame='icrs')\\n >>> c = SkyCoord(frame=ICRS, ra=ra, dec=dec, obstime='2001-01-02T12:34:56')\\n\\n >>> c = FK4(1 * u.deg, 2 * u.deg) # Uses defaults for obstime, equinox\\n >>> c = SkyCoord(c, obstime='J2010.11', equinox='B1965') # Override defaults\\n\\n >>> c = SkyCoord(w=0, u=1, v=2, unit='kpc', frame='galactic',\\n ... representation_type='cartesian')\\n\\n >>> c = SkyCoord([ICRS(ra=1*u.deg, dec=2*u.deg), ICRS(ra=3*u.deg, dec=4*u.deg)])\\n\\n Velocity components (proper motions or radial velocities) can also be\\n provided in a similar manner::\\n\\n >>> c = SkyCoord(ra=1*u.deg, dec=2*u.deg, radial_velocity=10*u.km/u.s)\\n\\n >>> c = SkyCoord(ra=1*u.deg, dec=2*u.deg, pm_ra_cosdec=2*u.mas/u.yr, pm_dec=1*u.mas/u.yr)\\n\\n As shown, the frame can be a `~astropy.coordinates.BaseCoordinateFrame`\\n class or the corresponding string alias. The frame classes that are built in\\n to astropy are `ICRS`, `FK5`, `FK4`, `FK4NoETerms`, and `Galactic`.\\n The string aliases are simply lower-case versions of the class name, and\\n allow for creating a `SkyCoord` object and transforming frames without\\n explicitly importing the frame classes.\\n\\n Parameters\\n ----------\\n frame : `~astropy.coordinates.BaseCoordinateFrame` class or string, optional\\n Type of coordinate frame this `SkyCoord` should represent. Defaults to\\n to ICRS if not given or given as None.\\n unit : `~astropy.units.Unit`, string, or tuple of :class:`~astropy.units.Unit` or str, optional\\n Units for supplied ``LON`` and ``LAT`` values, respectively. If\\n only one unit is supplied then it applies to both ``LON`` and\\n ``LAT``.\\n obstime : valid `~astropy.time.Time` initializer, optional\\n Time(s) of observation.\\n equinox : valid `~astropy.time.Time` initializer, optional\\n Coordinate frame equinox.\\n representation_type : str or Representation class\\n Specifies the representation, e.g. 'spherical', 'cartesian', or\\n 'cylindrical'. This affects the positional args and other keyword args\\n which must correspond to the given representation.\\n copy : bool, optional\\n If `True` (default), a copy of any coordinate data is made. This\\n argument can only be passed in as a keyword argument.\\n **keyword_args\\n Other keyword arguments as applicable for user-defined coordinate frames.\\n Common options include:\\n\\n ra, dec : valid `~astropy.coordinates.Angle` initializer, optional\\n RA and Dec for frames where ``ra`` and ``dec`` are keys in the\\n frame's ``representation_component_names``, including `ICRS`,\\n `FK5`, `FK4`, and `FK4NoETerms`.\\n pm_ra_cosdec, pm_dec : `~astropy.units.Quantity`, optional\\n Proper motion components, in angle per time units.\\n l, b : valid `~astropy.coordinates.Angle` initializer, optional\\n Galactic ``l`` and ``b`` for for frames where ``l`` and ``b`` are\\n keys in the frame's ``representation_component_names``, including\\n the `Galactic` frame.\\n pm_l_cosb, pm_b : `~astropy.units.Quantity`, optional\\n Proper motion components in the `Galactic` frame, in angle per time\\n units.\\n x, y, z : float or `~astropy.units.Quantity`, optional\\n Cartesian coordinates values\\n u, v, w : float or `~astropy.units.Quantity`, optional\\n Cartesian coordinates values for the Galactic frame.\\n radial_velocity : `~astropy.units.Quantity`, optional\\n The component of the velocity along the line-of-sight (i.e., the\\n radial direction), in velocity units.\\n \\\"\\\"\\\"\\n\\n # Declare that SkyCoord can be used as a Table column by defining the\\n # info property.\\n info = SkyCoordInfo()\\n\\n def __init__(self, *args, copy=True, **kwargs):\\n\\n # these are frame attributes set on this SkyCoord but *not* a part of\\n # the frame object this SkyCoord contains\\n self._extra_frameattr_names = set()\\n\\n # If all that is passed in is a frame instance that already has data,\\n # we should bypass all of the parsing and logic below. This is here\\n # to make this the fastest way to create a SkyCoord instance. Many of\\n # the classmethods implemented for performance enhancements will use\\n # this as the initialization path\\n if (len(args) == 1 and len(kwargs) == 0\\n and isinstance(args[0], (BaseCoordinateFrame, SkyCoord))):\\n\\n coords = args[0]\\n if isinstance(coords, SkyCoord):\\n self._extra_frameattr_names = coords._extra_frameattr_names\\n self.info = coords.info\\n\\n # Copy over any extra frame attributes\\n for attr_name in self._extra_frameattr_names:\\n # Setting it will also validate it.\\n setattr(self, attr_name, getattr(coords, attr_name))\\n\\n coords = coords.frame\\n\\n if not coords.has_data:\\n raise ValueError('Cannot initialize from a coordinate frame '\\n 'instance without coordinate data')\\n\\n if copy:\\n self._sky_coord_frame = coords.copy()\\n else:\\n self._sky_coord_frame = coords\\n\\n else:\\n # Get the frame instance without coordinate data but with all frame\\n # attributes set - these could either have been passed in with the\\n # frame as an instance, or passed in as kwargs here\\n frame_cls, frame_kwargs = _get_frame_without_data(args, kwargs)\\n\\n # Parse the args and kwargs to assemble a sanitized and validated\\n # kwargs dict for initializing attributes for this object and for\\n # creating the internal self._sky_coord_frame object\\n args = list(args) # Make it mutable\\n skycoord_kwargs, components, info = _parse_coordinate_data(\\n frame_cls(**frame_kwargs), args, kwargs)\\n\\n # In the above two parsing functions, these kwargs were identified\\n # as valid frame attributes for *some* frame, but not the frame that\\n # this SkyCoord will have. We keep these attributes as special\\n # skycoord frame attributes:\\n for attr in skycoord_kwargs:\\n # Setting it will also validate it.\\n setattr(self, attr, skycoord_kwargs[attr])\\n\\n if info is not None:\\n self.info = info\\n\\n # Finally make the internal coordinate object.\\n frame_kwargs.update(components)\\n self._sky_coord_frame = frame_cls(copy=copy, **frame_kwargs)\\n\\n if not self._sky_coord_frame.has_data:\\n raise ValueError('Cannot create a SkyCoord without data')\\n\\n @property\\n def frame(self):\\n return self._sky_coord_frame\\n\\n @property\\n def representation_type(self):\\n return self.frame.representation_type\\n\\n @representation_type.setter\\n def representation_type(self, value):\\n self.frame.representation_type = value\\n\\n # TODO: remove these in future\\n @property\\n def representation(self):\\n return self.frame.representation\\n\\n @representation.setter\\n def representation(self, value):\\n self.frame.representation = value\\n\\n @property\\n def shape(self):\\n return self.frame.shape\\n\\n def __eq__(self, value):\\n \\\"\\\"\\\"Equality operator for SkyCoord\\n\\n This implements strict equality and requires that the frames are\\n equivalent, extra frame attributes are equivalent, and that the\\n representation data are exactly equal.\\n \\\"\\\"\\\"\\n # Make sure that any extra frame attribute names are equivalent.\\n for attr in self._extra_frameattr_names | value._extra_frameattr_names:\\n if not self.frame._frameattr_equiv(getattr(self, attr),\\n getattr(value, attr)):\\n raise ValueError(f\\\"cannot compare: extra frame attribute \\\"\\n f\\\"'{attr}' is not equivalent \\\"\\n f\\\"(perhaps compare the frames directly to avoid \\\"\\n f\\\"this exception)\\\")\\n\\n return self._sky_coord_frame == value._sky_coord_frame\\n\\n def __ne__(self, value):\\n return np.logical_not(self == value)\\n\\n def _apply(self, method, *args, **kwargs):\\n \\\"\\\"\\\"Create a new instance, applying a method to the underlying data.\\n\\n In typical usage, the method is any of the shape-changing methods for\\n `~numpy.ndarray` (``reshape``, ``swapaxes``, etc.), as well as those\\n picking particular elements (``__getitem__``, ``take``, etc.), which\\n are all defined in `~astropy.utils.shapes.ShapedLikeNDArray`. It will be\\n applied to the underlying arrays in the representation (e.g., ``x``,\\n ``y``, and ``z`` for `~astropy.coordinates.CartesianRepresentation`),\\n as well as to any frame attributes that have a shape, with the results\\n used to create a new instance.\\n\\n Internally, it is also used to apply functions to the above parts\\n (in particular, `~numpy.broadcast_to`).\\n\\n Parameters\\n ----------\\n method : str or callable\\n If str, it is the name of a method that is applied to the internal\\n ``components``. If callable, the function is applied.\\n args : tuple\\n Any positional arguments for ``method``.\\n kwargs : dict\\n Any keyword arguments for ``method``.\\n \\\"\\\"\\\"\\n def apply_method(value):\\n if isinstance(value, ShapedLikeNDArray):\\n return value._apply(method, *args, **kwargs)\\n else:\\n if callable(method):\\n return method(value, *args, **kwargs)\\n else:\\n return getattr(value, method)(*args, **kwargs)\\n\\n # create a new but empty instance, and copy over stuff\\n new = super().__new__(self.__class__)\\n new._sky_coord_frame = self._sky_coord_frame._apply(method,\\n *args, **kwargs)\\n new._extra_frameattr_names = self._extra_frameattr_names.copy()\\n for attr in self._extra_frameattr_names:\\n value = getattr(self, attr)\\n if getattr(value, 'shape', ()):\\n value = apply_method(value)\\n elif method == 'copy' or method == 'flatten':\\n # flatten should copy also for a single element array, but\\n # we cannot use it directly for array scalars, since it\\n # always returns a one-dimensional array. So, just copy.\\n value = copy.copy(value)\\n setattr(new, '_' + attr, value)\\n\\n # Copy other 'info' attr only if it has actually been defined.\\n # See PR #3898 for further explanation and justification, along\\n # with Quantity.__array_finalize__\\n if 'info' in self.__dict__:\\n new.info = self.info\\n\\n return new\\n\\n def __setitem__(self, item, value):\\n \\\"\\\"\\\"Implement self[item] = value for SkyCoord\\n\\n The right hand ``value`` must be strictly consistent with self:\\n - Identical class\\n - Equivalent frames\\n - Identical representation_types\\n - Identical representation differentials keys\\n - Identical frame attributes\\n - Identical \\\"extra\\\" frame attributes (e.g. obstime for an ICRS coord)\\n\\n With these caveats the setitem ends up as effectively a setitem on\\n the representation data.\\n\\n self.frame.data[item] = value.frame.data\\n \\\"\\\"\\\"\\n if self.__class__ is not value.__class__:\\n raise TypeError(f'can only set from object of same class: '\\n f'{self.__class__.__name__} vs. '\\n f'{value.__class__.__name__}')\\n\\n # Make sure that any extra frame attribute names are equivalent.\\n for attr in self._extra_frameattr_names | value._extra_frameattr_names:\\n if not self.frame._frameattr_equiv(getattr(self, attr),\\n getattr(value, attr)):\\n raise ValueError(f'attribute {attr} is not equivalent')\\n\\n # Set the frame values. This checks frame equivalence and also clears\\n # the cache to ensure that the object is not in an inconsistent state.\\n self._sky_coord_frame[item] = value._sky_coord_frame\\n\\n def insert(self, obj, values, axis=0):\\n \\\"\\\"\\\"\\n Insert coordinate values before the given indices in the object and\\n return a new Frame object.\\n\\n The values to be inserted must conform to the rules for in-place setting\\n of ``SkyCoord`` objects.\\n\\n The API signature matches the ``np.insert`` API, but is more limited.\\n The specification of insert index ``obj`` must be a single integer,\\n and the ``axis`` must be ``0`` for simple insertion before the index.\\n\\n Parameters\\n ----------\\n obj : int\\n Integer index before which ``values`` is inserted.\\n values : array_like\\n Value(s) to insert. If the type of ``values`` is different\\n from that of quantity, ``values`` is converted to the matching type.\\n axis : int, optional\\n Axis along which to insert ``values``. Default is 0, which is the\\n only allowed value and will insert a row.\\n\\n Returns\\n -------\\n out : `~astropy.coordinates.SkyCoord` instance\\n New coordinate object with inserted value(s)\\n\\n \\\"\\\"\\\"\\n # Validate inputs: obj arg is integer, axis=0, self is not a scalar, and\\n # input index is in bounds.\\n try:\\n idx0 = operator.index(obj)\\n except TypeError:\\n raise TypeError('obj arg must be an integer')\\n\\n if axis != 0:\\n raise ValueError('axis must be 0')\\n\\n if not self.shape:\\n raise TypeError('cannot insert into scalar {} object'\\n .format(self.__class__.__name__))\\n\\n if abs(idx0) > len(self):\\n raise IndexError('index {} is out of bounds for axis 0 with size {}'\\n .format(idx0, len(self)))\\n\\n # Turn negative index into positive\\n if idx0 < 0:\\n idx0 = len(self) + idx0\\n\\n n_values = len(values) if values.shape else 1\\n\\n # Finally make the new object with the correct length and set values for the\\n # three sections, before insert, the insert, and after the insert.\\n out = self.__class__.info.new_like([self], len(self) + n_values, name=self.info.name)\\n\\n # Set the output values. This is where validation of `values` takes place to ensure\\n # that it can indeed be inserted.\\n out[:idx0] = self[:idx0]\\n out[idx0:idx0 + n_values] = values\\n out[idx0 + n_values:] = self[idx0:]\\n\\n return out\\n\\n def transform_to(self, frame, merge_attributes=True):\\n \\\"\\\"\\\"Transform this coordinate to a new frame.\\n\\n The precise frame transformed to depends on ``merge_attributes``.\\n If `False`, the destination frame is used exactly as passed in.\\n But this is often not quite what one wants. E.g., suppose one wants to\\n transform an ICRS coordinate that has an obstime attribute to FK4; in\\n this case, one likely would want to use this information. Thus, the\\n default for ``merge_attributes`` is `True`, in which the precedence is\\n as follows: (1) explicitly set (i.e., non-default) values in the\\n destination frame; (2) explicitly set values in the source; (3) default\\n value in the destination frame.\\n\\n Note that in either case, any explicitly set attributes on the source\\n `SkyCoord` that are not part of the destination frame's definition are\\n kept (stored on the resulting `SkyCoord`), and thus one can round-trip\\n (e.g., from FK4 to ICRS to FK4 without loosing obstime).\\n\\n Parameters\\n ----------\\n frame : str, `BaseCoordinateFrame` class or instance, or `SkyCoord` instance\\n The frame to transform this coordinate into. If a `SkyCoord`, the\\n underlying frame is extracted, and all other information ignored.\\n merge_attributes : bool, optional\\n Whether the default attributes in the destination frame are allowed\\n to be overridden by explicitly set attributes in the source\\n (see note above; default: `True`).\\n\\n Returns\\n -------\\n coord : `SkyCoord`\\n A new object with this coordinate represented in the `frame` frame.\\n\\n Raises\\n ------\\n ValueError\\n If there is no possible transformation route.\\n\\n \\\"\\\"\\\"\\n from astropy.coordinates.errors import ConvertError\\n\\n frame_kwargs = {}\\n\\n # Frame name (string) or frame class? Coerce into an instance.\\n try:\\n frame = _get_frame_class(frame)()\\n except Exception:\\n pass\\n\\n if isinstance(frame, SkyCoord):\\n frame = frame.frame # Change to underlying coord frame instance\\n\\n if isinstance(frame, BaseCoordinateFrame):\\n new_frame_cls = frame.__class__\\n # Get frame attributes, allowing defaults to be overridden by\\n # explicitly set attributes of the source if ``merge_attributes``.\\n for attr in frame_transform_graph.frame_attributes:\\n self_val = getattr(self, attr, None)\\n frame_val = getattr(frame, attr, None)\\n if (frame_val is not None\\n and not (merge_attributes\\n and frame.is_frame_attr_default(attr))):\\n frame_kwargs[attr] = frame_val\\n elif (self_val is not None\\n and not self.is_frame_attr_default(attr)):\\n frame_kwargs[attr] = self_val\\n elif frame_val is not None:\\n frame_kwargs[attr] = frame_val\\n else:\\n raise ValueError('Transform `frame` must be a frame name, class, or instance')\\n\\n # Get the composite transform to the new frame\\n trans = frame_transform_graph.get_transform(self.frame.__class__, new_frame_cls)\\n if trans is None:\\n raise ConvertError('Cannot transform from {} to {}'\\n .format(self.frame.__class__, new_frame_cls))\\n\\n # Make a generic frame which will accept all the frame kwargs that\\n # are provided and allow for transforming through intermediate frames\\n # which may require one or more of those kwargs.\\n generic_frame = GenericFrame(frame_kwargs)\\n\\n # Do the transformation, returning a coordinate frame of the desired\\n # final type (not generic).\\n new_coord = trans(self.frame, generic_frame)\\n\\n # Finally make the new SkyCoord object from the `new_coord` and\\n # remaining frame_kwargs that are not frame_attributes in `new_coord`.\\n for attr in (set(new_coord.get_frame_attr_names()) &\\n set(frame_kwargs.keys())):\\n frame_kwargs.pop(attr)\\n\\n return self.__class__(new_coord, **frame_kwargs)\\n\\n def apply_space_motion(self, new_obstime=None, dt=None):\\n \\\"\\\"\\\"\\n Compute the position of the source represented by this coordinate object\\n to a new time using the velocities stored in this object and assuming\\n linear space motion (including relativistic corrections). This is\\n sometimes referred to as an \\\"epoch transformation.\\\"\\n\\n The initial time before the evolution is taken from the ``obstime``\\n attribute of this coordinate. Note that this method currently does not\\n support evolving coordinates where the *frame* has an ``obstime`` frame\\n attribute, so the ``obstime`` is only used for storing the before and\\n after times, not actually as an attribute of the frame. Alternatively,\\n if ``dt`` is given, an ``obstime`` need not be provided at all.\\n\\n Parameters\\n ----------\\n new_obstime : `~astropy.time.Time`, optional\\n The time at which to evolve the position to. Requires that the\\n ``obstime`` attribute be present on this frame.\\n dt : `~astropy.units.Quantity`, `~astropy.time.TimeDelta`, optional\\n An amount of time to evolve the position of the source. Cannot be\\n given at the same time as ``new_obstime``.\\n\\n Returns\\n -------\\n new_coord : `SkyCoord`\\n A new coordinate object with the evolved location of this coordinate\\n at the new time. ``obstime`` will be set on this object to the new\\n time only if ``self`` also has ``obstime``.\\n \\\"\\\"\\\"\\n\\n if (new_obstime is None and dt is None or\\n new_obstime is not None and dt is not None):\\n raise ValueError(\\\"You must specify one of `new_obstime` or `dt`, \\\"\\n \\\"but not both.\\\")\\n\\n # Validate that we have velocity info\\n if 's' not in self.frame.data.differentials:\\n raise ValueError('SkyCoord requires velocity data to evolve the '\\n 'position.')\\n\\n if 'obstime' in self.frame.frame_attributes:\\n raise NotImplementedError(\\\"Updating the coordinates in a frame \\\"\\n \\\"with explicit time dependence is \\\"\\n \\\"currently not supported. If you would \\\"\\n \\\"like this functionality, please open an \\\"\\n \\\"issue on github:\\\\n\\\"\\n \\\"https://github.com/astropy/astropy\\\")\\n\\n if new_obstime is not None and self.obstime is None:\\n # If no obstime is already on this object, raise an error if a new\\n # obstime is passed: we need to know the time / epoch at which the\\n # the position / velocity were measured initially\\n raise ValueError('This object has no associated `obstime`. '\\n 'apply_space_motion() must receive a time '\\n 'difference, `dt`, and not a new obstime.')\\n\\n # Compute t1 and t2, the times used in the starpm call, which *only*\\n # uses them to compute a delta-time\\n t1 = self.obstime\\n if dt is None:\\n # self.obstime is not None and new_obstime is not None b/c of above\\n # checks\\n t2 = new_obstime\\n else:\\n # new_obstime is definitely None b/c of the above checks\\n if t1 is None:\\n # MAGIC NUMBER: if the current SkyCoord object has no obstime,\\n # assume J2000 to do the dt offset. This is not actually used\\n # for anything except a delta-t in starpm, so it's OK that it's\\n # not necessarily the \\\"real\\\" obstime\\n t1 = Time('J2000')\\n new_obstime = None # we don't actually know the inital obstime\\n t2 = t1 + dt\\n else:\\n t2 = t1 + dt\\n new_obstime = t2\\n # starpm wants tdb time\\n t1 = t1.tdb\\n t2 = t2.tdb\\n\\n # proper motion in RA should not include the cos(dec) term, see the\\n # erfa function eraStarpv, comment (4). So we convert to the regular\\n # spherical differentials.\\n icrsrep = self.icrs.represent_as(SphericalRepresentation, SphericalDifferential)\\n icrsvel = icrsrep.differentials['s']\\n\\n parallax_zero = False\\n try:\\n plx = icrsrep.distance.to_value(u.arcsecond, u.parallax())\\n except u.UnitConversionError: # No distance: set to 0 by convention\\n plx = 0.\\n parallax_zero = True\\n\\n try:\\n rv = icrsvel.d_distance.to_value(u.km/u.s)\\n except u.UnitConversionError: # No RV\\n rv = 0.\\n\\n starpm = erfa.pmsafe(icrsrep.lon.radian, icrsrep.lat.radian,\\n icrsvel.d_lon.to_value(u.radian/u.yr),\\n icrsvel.d_lat.to_value(u.radian/u.yr),\\n plx, rv, t1.jd1, t1.jd2, t2.jd1, t2.jd2)\\n\\n if parallax_zero:\\n new_distance = None\\n else:\\n new_distance = Distance(parallax=starpm[4] << u.arcsec)\\n\\n icrs2 = ICRS(ra=u.Quantity(starpm[0], u.radian, copy=False),\\n dec=u.Quantity(starpm[1], u.radian, copy=False),\\n pm_ra=u.Quantity(starpm[2], u.radian/u.yr, copy=False),\\n pm_dec=u.Quantity(starpm[3], u.radian/u.yr, copy=False),\\n distance=new_distance,\\n radial_velocity=u.Quantity(starpm[5], u.km/u.s, copy=False),\\n differential_type=SphericalDifferential)\\n\\n # Update the obstime of the returned SkyCoord, and need to carry along\\n # the frame attributes\\n frattrs = {attrnm: getattr(self, attrnm)\\n for attrnm in self._extra_frameattr_names}\\n frattrs['obstime'] = new_obstime\\n return self.__class__(icrs2, **frattrs).transform_to(self.frame)\\n\\n def _is_name(self, string):\\n \\\"\\\"\\\"\\n Returns whether a string is one of the aliases for the frame.\\n \\\"\\\"\\\"\\n return (self.frame.name == string or\\n (isinstance(self.frame.name, list) and string in self.frame.name))\\n\\n def __getattr__(self, attr):\\n \\\"\\\"\\\"\\n Overrides getattr to return coordinates that this can be transformed\\n to, based on the alias attr in the master transform graph.\\n \\\"\\\"\\\"\\n if '_sky_coord_frame' in self.__dict__:\\n if self._is_name(attr):\\n return self # Should this be a deepcopy of self?\\n\\n # Anything in the set of all possible frame_attr_names is handled\\n # here. If the attr is relevant for the current frame then delegate\\n # to self.frame otherwise get it from self._<attr>.\\n if attr in frame_transform_graph.frame_attributes:\\n if attr in self.frame.get_frame_attr_names():\\n return getattr(self.frame, attr)\\n else:\\n return getattr(self, '_' + attr, None)\\n\\n # Some attributes might not fall in the above category but still\\n # are available through self._sky_coord_frame.\\n if not attr.startswith('_') and hasattr(self._sky_coord_frame, attr):\\n return getattr(self._sky_coord_frame, attr)\\n\\n # Try to interpret as a new frame for transforming.\\n frame_cls = frame_transform_graph.lookup_name(attr)\\n if frame_cls is not None and self.frame.is_transformable_to(frame_cls):\\n return self.transform_to(attr)\\n\\n # Fail\\n raise AttributeError(\\\"'{}' object has no attribute '{}'\\\"\\n .format(self.__class__.__name__, attr))\\n\\n def __setattr__(self, attr, val):\\n # This is to make anything available through __getattr__ immutable\\n if '_sky_coord_frame' in self.__dict__:\\n if self._is_name(attr):\\n raise AttributeError(f\\\"'{attr}' is immutable\\\")\\n\\n if not attr.startswith('_') and hasattr(self._sky_coord_frame, attr):\\n setattr(self._sky_coord_frame, attr, val)\\n return\\n\\n frame_cls = frame_transform_graph.lookup_name(attr)\\n if frame_cls is not None and self.frame.is_transformable_to(frame_cls):\\n raise AttributeError(f\\\"'{attr}' is immutable\\\")\\n\\n if attr in frame_transform_graph.frame_attributes:\\n # All possible frame attributes can be set, but only via a private\\n # variable. See __getattr__ above.\\n super().__setattr__('_' + attr, val)\\n # Validate it\\n frame_transform_graph.frame_attributes[attr].__get__(self)\\n # And add to set of extra attributes\\n self._extra_frameattr_names |= {attr}\\n\\n else:\\n # Otherwise, do the standard Python attribute setting\\n super().__setattr__(attr, val)\\n\\n def __delattr__(self, attr):\\n # mirror __setattr__ above\\n if '_sky_coord_frame' in self.__dict__:\\n if self._is_name(attr):\\n raise AttributeError(f\\\"'{attr}' is immutable\\\")\\n\\n if not attr.startswith('_') and hasattr(self._sky_coord_frame,\\n attr):\\n delattr(self._sky_coord_frame, attr)\\n return\\n\\n frame_cls = frame_transform_graph.lookup_name(attr)\\n if frame_cls is not None and self.frame.is_transformable_to(frame_cls):\\n raise AttributeError(f\\\"'{attr}' is immutable\\\")\\n\\n if attr in frame_transform_graph.frame_attributes:\\n # All possible frame attributes can be deleted, but need to remove\\n # the corresponding private variable. See __getattr__ above.\\n super().__delattr__('_' + attr)\\n # Also remove it from the set of extra attributes\\n self._extra_frameattr_names -= {attr}\\n\\n else:\\n # Otherwise, do the standard Python attribute setting\\n super().__delattr__(attr)\\n\\n @override__dir__\\n def __dir__(self):\\n \\\"\\\"\\\"\\n Override the builtin `dir` behavior to include:\\n - Transforms available by aliases\\n - Attribute / methods of the underlying self.frame object\\n \\\"\\\"\\\"\\n\\n # determine the aliases that this can be transformed to.\\n dir_values = set()\\n for name in frame_transform_graph.get_names():\\n frame_cls = frame_transform_graph.lookup_name(name)\\n if self.frame.is_transformable_to(frame_cls):\\n dir_values.add(name)\\n\\n # Add public attributes of self.frame\\n dir_values.update(set(attr for attr in dir(self.frame) if not attr.startswith('_')))\\n\\n # Add all possible frame attributes\\n dir_values.update(frame_transform_graph.frame_attributes.keys())\\n\\n return dir_values\\n\\n def __repr__(self):\\n clsnm = self.__class__.__name__\\n coonm = self.frame.__class__.__name__\\n frameattrs = self.frame._frame_attrs_repr()\\n if frameattrs:\\n frameattrs = ': ' + frameattrs\\n\\n data = self.frame._data_repr()\\n if data:\\n data = ': ' + data\\n\\n return '<{clsnm} ({coonm}{frameattrs}){data}>'.format(**locals())\\n\\n def to_string(self, style='decimal', **kwargs):\\n \\\"\\\"\\\"\\n A string representation of the coordinates.\\n\\n The default styles definitions are::\\n\\n 'decimal': 'lat': {'decimal': True, 'unit': \\\"deg\\\"}\\n 'lon': {'decimal': True, 'unit': \\\"deg\\\"}\\n 'dms': 'lat': {'unit': \\\"deg\\\"}\\n 'lon': {'unit': \\\"deg\\\"}\\n 'hmsdms': 'lat': {'alwayssign': True, 'pad': True, 'unit': \\\"deg\\\"}\\n 'lon': {'pad': True, 'unit': \\\"hour\\\"}\\n\\n See :meth:`~astropy.coordinates.Angle.to_string` for details and\\n keyword arguments (the two angles forming the coordinates are are\\n both :class:`~astropy.coordinates.Angle` instances). Keyword\\n arguments have precedence over the style defaults and are passed\\n to :meth:`~astropy.coordinates.Angle.to_string`.\\n\\n Parameters\\n ----------\\n style : {'hmsdms', 'dms', 'decimal'}\\n The formatting specification to use. These encode the three most\\n common ways to represent coordinates. The default is `decimal`.\\n kwargs\\n Keyword args passed to :meth:`~astropy.coordinates.Angle.to_string`.\\n \\\"\\\"\\\"\\n\\n sph_coord = self.frame.represent_as(SphericalRepresentation)\\n\\n styles = {'hmsdms': {'lonargs': {'unit': u.hour, 'pad': True},\\n 'latargs': {'unit': u.degree, 'pad': True, 'alwayssign': True}},\\n 'dms': {'lonargs': {'unit': u.degree},\\n 'latargs': {'unit': u.degree}},\\n 'decimal': {'lonargs': {'unit': u.degree, 'decimal': True},\\n 'latargs': {'unit': u.degree, 'decimal': True}}\\n }\\n\\n lonargs = {}\\n latargs = {}\\n\\n if style in styles:\\n lonargs.update(styles[style]['lonargs'])\\n latargs.update(styles[style]['latargs'])\\n else:\\n raise ValueError('Invalid style. Valid options are: {}'.format(\\\",\\\".join(styles)))\\n\\n lonargs.update(kwargs)\\n latargs.update(kwargs)\\n\\n if np.isscalar(sph_coord.lon.value):\\n coord_string = (sph_coord.lon.to_string(**lonargs) +\\n \\\" \\\" + sph_coord.lat.to_string(**latargs))\\n else:\\n coord_string = []\\n for lonangle, latangle in zip(sph_coord.lon.ravel(), sph_coord.lat.ravel()):\\n coord_string += [(lonangle.to_string(**lonargs) +\\n \\\" \\\" + latangle.to_string(**latargs))]\\n if len(sph_coord.shape) > 1:\\n coord_string = np.array(coord_string).reshape(sph_coord.shape)\\n\\n return coord_string\\n\\n def is_equivalent_frame(self, other):\\n \\\"\\\"\\\"\\n Checks if this object's frame as the same as that of the ``other``\\n object.\\n\\n To be the same frame, two objects must be the same frame class and have\\n the same frame attributes. For two `SkyCoord` objects, *all* of the\\n frame attributes have to match, not just those relevant for the object's\\n frame.\\n\\n Parameters\\n ----------\\n other : SkyCoord or BaseCoordinateFrame\\n The other object to check.\\n\\n Returns\\n -------\\n isequiv : bool\\n True if the frames are the same, False if not.\\n\\n Raises\\n ------\\n TypeError\\n If ``other`` isn't a `SkyCoord` or a `BaseCoordinateFrame` or subclass.\\n \\\"\\\"\\\"\\n if isinstance(other, BaseCoordinateFrame):\\n return self.frame.is_equivalent_frame(other)\\n elif isinstance(other, SkyCoord):\\n if other.frame.name != self.frame.name:\\n return False\\n\\n for fattrnm in frame_transform_graph.frame_attributes:\\n if not BaseCoordinateFrame._frameattr_equiv(getattr(self, fattrnm),\\n getattr(other, fattrnm)):\\n return False\\n return True\\n else:\\n # not a BaseCoordinateFrame nor a SkyCoord object\\n raise TypeError(\\\"Tried to do is_equivalent_frame on something that \\\"\\n \\\"isn't frame-like\\\")\\n\\n # High-level convenience methods\\n def separation(self, other):\\n \\\"\\\"\\\"\\n Computes on-sky separation between this coordinate and another.\\n\\n .. note::\\n\\n If the ``other`` coordinate object is in a different frame, it is\\n first transformed to the frame of this object. This can lead to\\n unintuitive behavior if not accounted for. Particularly of note is\\n that ``self.separation(other)`` and ``other.separation(self)`` may\\n not give the same answer in this case.\\n\\n For more on how to use this (and related) functionality, see the\\n examples in :doc:`/coordinates/matchsep`.\\n\\n Parameters\\n ----------\\n other : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\\n The coordinate to get the separation to.\\n\\n Returns\\n -------\\n sep : `~astropy.coordinates.Angle`\\n The on-sky separation between this and the ``other`` coordinate.\\n\\n Notes\\n -----\\n The separation is calculated using the Vincenty formula, which\\n is stable at all locations, including poles and antipodes [1]_.\\n\\n .. [1] https://en.wikipedia.org/wiki/Great-circle_distance\\n\\n \\\"\\\"\\\"\\n from . import Angle\\n from .angle_utilities import angular_separation\\n\\n if not self.is_equivalent_frame(other):\\n try:\\n kwargs = {'merge_attributes': False} if isinstance(other, SkyCoord) else {}\\n other = other.transform_to(self, **kwargs)\\n except TypeError:\\n raise TypeError('Can only get separation to another SkyCoord '\\n 'or a coordinate frame with data')\\n\\n lon1 = self.spherical.lon\\n lat1 = self.spherical.lat\\n lon2 = other.spherical.lon\\n lat2 = other.spherical.lat\\n\\n # Get the separation as a Quantity, convert to Angle in degrees\\n sep = angular_separation(lon1, lat1, lon2, lat2)\\n return Angle(sep, unit=u.degree)\\n\\n def separation_3d(self, other):\\n \\\"\\\"\\\"\\n Computes three dimensional separation between this coordinate\\n and another.\\n\\n For more on how to use this (and related) functionality, see the\\n examples in :doc:`/coordinates/matchsep`.\\n\\n Parameters\\n ----------\\n other : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\\n The coordinate to get the separation to.\\n\\n Returns\\n -------\\n sep : `~astropy.coordinates.Distance`\\n The real-space distance between these two coordinates.\\n\\n Raises\\n ------\\n ValueError\\n If this or the other coordinate do not have distances.\\n \\\"\\\"\\\"\\n if not self.is_equivalent_frame(other):\\n try:\\n kwargs = {'merge_attributes': False} if isinstance(other, SkyCoord) else {}\\n other = other.transform_to(self, **kwargs)\\n except TypeError:\\n raise TypeError('Can only get separation to another SkyCoord '\\n 'or a coordinate frame with data')\\n\\n if issubclass(self.data.__class__, UnitSphericalRepresentation):\\n raise ValueError('This object does not have a distance; cannot '\\n 'compute 3d separation.')\\n if issubclass(other.data.__class__, UnitSphericalRepresentation):\\n raise ValueError('The other object does not have a distance; '\\n 'cannot compute 3d separation.')\\n\\n c1 = self.cartesian.without_differentials()\\n c2 = other.cartesian.without_differentials()\\n return Distance((c1 - c2).norm())\\n\\n def spherical_offsets_to(self, tocoord):\\n r\\\"\\\"\\\"\\n Computes angular offsets to go *from* this coordinate *to* another.\\n\\n Parameters\\n ----------\\n tocoord : `~astropy.coordinates.BaseCoordinateFrame`\\n The coordinate to find the offset to.\\n\\n Returns\\n -------\\n lon_offset : `~astropy.coordinates.Angle`\\n The angular offset in the longitude direction (i.e., RA for\\n equatorial coordinates).\\n lat_offset : `~astropy.coordinates.Angle`\\n The angular offset in the latitude direction (i.e., Dec for\\n equatorial coordinates).\\n\\n Raises\\n ------\\n ValueError\\n If the ``tocoord`` is not in the same frame as this one. This is\\n different from the behavior of the `separation`/`separation_3d`\\n methods because the offset components depend critically on the\\n specific choice of frame.\\n\\n Notes\\n -----\\n This uses the sky offset frame machinery, and hence will produce a new\\n sky offset frame if one does not already exist for this object's frame\\n class.\\n\\n See Also\\n --------\\n separation : for the *total* angular offset (not broken out into components).\\n position_angle : for the direction of the offset.\\n\\n \\\"\\\"\\\"\\n if not self.is_equivalent_frame(tocoord):\\n raise ValueError('Tried to use spherical_offsets_to with two non-matching frames!')\\n\\n aframe = self.skyoffset_frame()\\n acoord = tocoord.transform_to(aframe)\\n\\n dlon = acoord.spherical.lon.view(Angle)\\n dlat = acoord.spherical.lat.view(Angle)\\n return dlon, dlat\\n\\n def directional_offset_by(self, position_angle, separation):\\n \\\"\\\"\\\"\\n Computes coordinates at the given offset from this coordinate.\\n\\n Parameters\\n ----------\\n position_angle : `~astropy.coordinates.Angle`\\n position_angle of offset\\n separation : `~astropy.coordinates.Angle`\\n offset angular separation\\n\\n Returns\\n -------\\n newpoints : `~astropy.coordinates.SkyCoord`\\n The coordinates for the location that corresponds to offsetting by\\n the given `position_angle` and `separation`.\\n\\n Notes\\n -----\\n Returned SkyCoord frame retains only the frame attributes that are for\\n the resulting frame type. (e.g. if the input frame is\\n `~astropy.coordinates.ICRS`, an ``equinox`` value will be retained, but\\n an ``obstime`` will not.)\\n\\n For a more complete set of transform offsets, use `~astropy.wcs.WCS`.\\n `~astropy.coordinates.SkyCoord.skyoffset_frame()` can also be used to\\n create a spherical frame with (lat=0, lon=0) at a reference point,\\n approximating an xy cartesian system for small offsets. This method\\n is distinct in that it is accurate on the sphere.\\n\\n See Also\\n --------\\n position_angle : inverse operation for the ``position_angle`` component\\n separation : inverse operation for the ``separation`` component\\n\\n \\\"\\\"\\\"\\n from . import angle_utilities\\n\\n slat = self.represent_as(UnitSphericalRepresentation).lat\\n slon = self.represent_as(UnitSphericalRepresentation).lon\\n\\n newlon, newlat = angle_utilities.offset_by(\\n lon=slon, lat=slat,\\n posang=position_angle, distance=separation)\\n\\n return SkyCoord(newlon, newlat, frame=self.frame)\\n\\n def match_to_catalog_sky(self, catalogcoord, nthneighbor=1):\\n \\\"\\\"\\\"\\n Finds the nearest on-sky matches of this coordinate in a set of\\n catalog coordinates.\\n\\n For more on how to use this (and related) functionality, see the\\n examples in :doc:`/coordinates/matchsep`.\\n\\n Parameters\\n ----------\\n catalogcoord : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\\n The base catalog in which to search for matches. Typically this\\n will be a coordinate object that is an array (i.e.,\\n ``catalogcoord.isscalar == False``)\\n nthneighbor : int, optional\\n Which closest neighbor to search for. Typically ``1`` is\\n desired here, as that is correct for matching one set of\\n coordinates to another. The next likely use case is ``2``,\\n for matching a coordinate catalog against *itself* (``1``\\n is inappropriate because each point will find itself as the\\n closest match).\\n\\n Returns\\n -------\\n idx : integer array\\n Indices into ``catalogcoord`` to get the matched points for\\n each of this object's coordinates. Shape matches this\\n object.\\n sep2d : `~astropy.coordinates.Angle`\\n The on-sky separation between the closest match for each\\n element in this object in ``catalogcoord``. Shape matches\\n this object.\\n dist3d : `~astropy.units.Quantity`\\n The 3D distance between the closest match for each element\\n in this object in ``catalogcoord``. Shape matches this\\n object. Unless both this and ``catalogcoord`` have associated\\n distances, this quantity assumes that all sources are at a\\n distance of 1 (dimensionless).\\n\\n Notes\\n -----\\n This method requires `SciPy <https://www.scipy.org/>`_ to be\\n installed or it will fail.\\n\\n See Also\\n --------\\n astropy.coordinates.match_coordinates_sky\\n SkyCoord.match_to_catalog_3d\\n \\\"\\\"\\\"\\n from .matching import match_coordinates_sky\\n\\n if (isinstance(catalogcoord, (SkyCoord, BaseCoordinateFrame))\\n and catalogcoord.has_data):\\n self_in_catalog_frame = self.transform_to(catalogcoord)\\n else:\\n raise TypeError('Can only get separation to another SkyCoord or a '\\n 'coordinate frame with data')\\n\\n res = match_coordinates_sky(self_in_catalog_frame, catalogcoord,\\n nthneighbor=nthneighbor,\\n storekdtree='_kdtree_sky')\\n return res\\n\\n def match_to_catalog_3d(self, catalogcoord, nthneighbor=1):\\n \\\"\\\"\\\"\\n Finds the nearest 3-dimensional matches of this coordinate to a set\\n of catalog coordinates.\\n\\n This finds the 3-dimensional closest neighbor, which is only different\\n from the on-sky distance if ``distance`` is set in this object or the\\n ``catalogcoord`` object.\\n\\n For more on how to use this (and related) functionality, see the\\n examples in :doc:`/coordinates/matchsep`.\\n\\n Parameters\\n ----------\\n catalogcoord : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\\n The base catalog in which to search for matches. Typically this\\n will be a coordinate object that is an array (i.e.,\\n ``catalogcoord.isscalar == False``)\\n nthneighbor : int, optional\\n Which closest neighbor to search for. Typically ``1`` is\\n desired here, as that is correct for matching one set of\\n coordinates to another. The next likely use case is\\n ``2``, for matching a coordinate catalog against *itself*\\n (``1`` is inappropriate because each point will find\\n itself as the closest match).\\n\\n Returns\\n -------\\n idx : integer array\\n Indices into ``catalogcoord`` to get the matched points for\\n each of this object's coordinates. Shape matches this\\n object.\\n sep2d : `~astropy.coordinates.Angle`\\n The on-sky separation between the closest match for each\\n element in this object in ``catalogcoord``. Shape matches\\n this object.\\n dist3d : `~astropy.units.Quantity`\\n The 3D distance between the closest match for each element\\n in this object in ``catalogcoord``. Shape matches this\\n object.\\n\\n Notes\\n -----\\n This method requires `SciPy <https://www.scipy.org/>`_ to be\\n installed or it will fail.\\n\\n See Also\\n --------\\n astropy.coordinates.match_coordinates_3d\\n SkyCoord.match_to_catalog_sky\\n \\\"\\\"\\\"\\n from .matching import match_coordinates_3d\\n\\n if (isinstance(catalogcoord, (SkyCoord, BaseCoordinateFrame))\\n and catalogcoord.has_data):\\n self_in_catalog_frame = self.transform_to(catalogcoord)\\n else:\\n raise TypeError('Can only get separation to another SkyCoord or a '\\n 'coordinate frame with data')\\n\\n res = match_coordinates_3d(self_in_catalog_frame, catalogcoord,\\n nthneighbor=nthneighbor,\\n storekdtree='_kdtree_3d')\\n\\n return res\\n\\n def search_around_sky(self, searcharoundcoords, seplimit):\\n \\\"\\\"\\\"\\n Searches for all coordinates in this object around a supplied set of\\n points within a given on-sky separation.\\n\\n This is intended for use on `~astropy.coordinates.SkyCoord` objects\\n with coordinate arrays, rather than a scalar coordinate. For a scalar\\n coordinate, it is better to use\\n `~astropy.coordinates.SkyCoord.separation`.\\n\\n For more on how to use this (and related) functionality, see the\\n examples in :doc:`/coordinates/matchsep`.\\n\\n Parameters\\n ----------\\n searcharoundcoords : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\\n The coordinates to search around to try to find matching points in\\n this `SkyCoord`. This should be an object with array coordinates,\\n not a scalar coordinate object.\\n seplimit : `~astropy.units.Quantity` with angle units\\n The on-sky separation to search within.\\n\\n Returns\\n -------\\n idxsearcharound : integer array\\n Indices into ``searcharoundcoords`` that match the\\n corresponding elements of ``idxself``. Shape matches\\n ``idxself``.\\n idxself : integer array\\n Indices into ``self`` that match the\\n corresponding elements of ``idxsearcharound``. Shape matches\\n ``idxsearcharound``.\\n sep2d : `~astropy.coordinates.Angle`\\n The on-sky separation between the coordinates. Shape matches\\n ``idxsearcharound`` and ``idxself``.\\n dist3d : `~astropy.units.Quantity`\\n The 3D distance between the coordinates. Shape matches\\n ``idxsearcharound`` and ``idxself``.\\n\\n Notes\\n -----\\n This method requires `SciPy <https://www.scipy.org/>`_ (>=0.12.0) to be\\n installed or it will fail.\\n\\n In the current implementation, the return values are always sorted in\\n the same order as the ``searcharoundcoords`` (so ``idxsearcharound`` is\\n in ascending order). This is considered an implementation detail,\\n though, so it could change in a future release.\\n\\n See Also\\n --------\\n astropy.coordinates.search_around_sky\\n SkyCoord.search_around_3d\\n \\\"\\\"\\\"\\n from .matching import search_around_sky\\n\\n return search_around_sky(searcharoundcoords, self, seplimit,\\n storekdtree='_kdtree_sky')\\n\\n def search_around_3d(self, searcharoundcoords, distlimit):\\n \\\"\\\"\\\"\\n Searches for all coordinates in this object around a supplied set of\\n points within a given 3D radius.\\n\\n This is intended for use on `~astropy.coordinates.SkyCoord` objects\\n with coordinate arrays, rather than a scalar coordinate. For a scalar\\n coordinate, it is better to use\\n `~astropy.coordinates.SkyCoord.separation_3d`.\\n\\n For more on how to use this (and related) functionality, see the\\n examples in :doc:`/coordinates/matchsep`.\\n\\n Parameters\\n ----------\\n searcharoundcoords : `~astropy.coordinates.SkyCoord` or `~astropy.coordinates.BaseCoordinateFrame`\\n The coordinates to search around to try to find matching points in\\n this `SkyCoord`. This should be an object with array coordinates,\\n not a scalar coordinate object.\\n distlimit : `~astropy.units.Quantity` with distance units\\n The physical radius to search within.\\n\\n Returns\\n -------\\n idxsearcharound : integer array\\n Indices into ``searcharoundcoords`` that match the\\n corresponding elements of ``idxself``. Shape matches\\n ``idxself``.\\n idxself : integer array\\n Indices into ``self`` that match the\\n corresponding elements of ``idxsearcharound``. Shape matches\\n ``idxsearcharound``.\\n sep2d : `~astropy.coordinates.Angle`\\n The on-sky separation between the coordinates. Shape matches\\n ``idxsearcharound`` and ``idxself``.\\n dist3d : `~astropy.units.Quantity`\\n The 3D distance between the coordinates. Shape matches\\n ``idxsearcharound`` and ``idxself``.\\n\\n Notes\\n -----\\n This method requires `SciPy <https://www.scipy.org/>`_ (>=0.12.0) to be\\n installed or it will fail.\\n\\n In the current implementation, the return values are always sorted in\\n the same order as the ``searcharoundcoords`` (so ``idxsearcharound`` is\\n in ascending order). This is considered an implementation detail,\\n though, so it could change in a future release.\\n\\n See Also\\n --------\\n astropy.coordinates.search_around_3d\\n SkyCoord.search_around_sky\\n \\\"\\\"\\\"\\n from .matching import search_around_3d\\n\\n return search_around_3d(searcharoundcoords, self, distlimit,\\n storekdtree='_kdtree_3d')\\n\\n def position_angle(self, other):\\n \\\"\\\"\\\"\\n Computes the on-sky position angle (East of North) between this\\n `SkyCoord` and another.\\n\\n Parameters\\n ----------\\n other : `SkyCoord`\\n The other coordinate to compute the position angle to. It is\\n treated as the \\\"head\\\" of the vector of the position angle.\\n\\n Returns\\n -------\\n pa : `~astropy.coordinates.Angle`\\n The (positive) position angle of the vector pointing from ``self``\\n to ``other``. If either ``self`` or ``other`` contain arrays, this\\n will be an array following the appropriate `numpy` broadcasting\\n rules.\\n\\n Examples\\n --------\\n\\n >>> c1 = SkyCoord(0*u.deg, 0*u.deg)\\n >>> c2 = SkyCoord(1*u.deg, 0*u.deg)\\n >>> c1.position_angle(c2).degree\\n 90.0\\n >>> c3 = SkyCoord(1*u.deg, 1*u.deg)\\n >>> c1.position_angle(c3).degree # doctest: +FLOAT_CMP\\n 44.995636455344844\\n \\\"\\\"\\\"\\n from . import angle_utilities\\n\\n if not self.is_equivalent_frame(other):\\n try:\\n other = other.transform_to(self, merge_attributes=False)\\n except TypeError:\\n raise TypeError('Can only get position_angle to another '\\n 'SkyCoord or a coordinate frame with data')\\n\\n slat = self.represent_as(UnitSphericalRepresentation).lat\\n slon = self.represent_as(UnitSphericalRepresentation).lon\\n olat = other.represent_as(UnitSphericalRepresentation).lat\\n olon = other.represent_as(UnitSphericalRepresentation).lon\\n\\n return angle_utilities.position_angle(slon, slat, olon, olat)\\n\\n def skyoffset_frame(self, rotation=None):\\n \\\"\\\"\\\"\\n Returns the sky offset frame with this `SkyCoord` at the origin.\\n\\n Returns\\n -------\\n astrframe : `~astropy.coordinates.SkyOffsetFrame`\\n A sky offset frame of the same type as this `SkyCoord` (e.g., if\\n this object has an ICRS coordinate, the resulting frame is\\n SkyOffsetICRS, with the origin set to this object)\\n rotation : `~astropy.coordinates.Angle` or `~astropy.units.Quantity` with angle units\\n The final rotation of the frame about the ``origin``. The sign of\\n the rotation is the left-hand rule. That is, an object at a\\n particular position angle in the un-rotated system will be sent to\\n the positive latitude (z) direction in the final frame.\\n \\\"\\\"\\\"\\n return SkyOffsetFrame(origin=self, rotation=rotation)\\n\\n def get_constellation(self, short_name=False, constellation_list='iau'):\\n \\\"\\\"\\\"\\n Determines the constellation(s) of the coordinates this `SkyCoord`\\n contains.\\n\\n Parameters\\n ----------\\n short_name : bool\\n If True, the returned names are the IAU-sanctioned abbreviated\\n names. Otherwise, full names for the constellations are used.\\n constellation_list : str\\n The set of constellations to use. Currently only ``'iau'`` is\\n supported, meaning the 88 \\\"modern\\\" constellations endorsed by the IAU.\\n\\n Returns\\n -------\\n constellation : str or string array\\n If this is a scalar coordinate, returns the name of the\\n constellation. If it is an array `SkyCoord`, it returns an array of\\n names.\\n\\n Notes\\n -----\\n To determine which constellation a point on the sky is in, this first\\n precesses to B1875, and then uses the Delporte boundaries of the 88\\n modern constellations, as tabulated by\\n `Roman 1987 <http://cdsarc.u-strasbg.fr/viz-bin/Cat?VI/42>`_.\\n\\n See Also\\n --------\\n astropy.coordinates.get_constellation\\n \\\"\\\"\\\"\\n from .funcs import get_constellation\\n\\n # because of issue #7028, the conversion to a PrecessedGeocentric\\n # system fails in some cases. Work around is to drop the velocities.\\n # they are not needed here since only position infromation is used\\n extra_frameattrs = {nm: getattr(self, nm)\\n for nm in self._extra_frameattr_names}\\n novel = SkyCoord(self.realize_frame(self.data.without_differentials()),\\n **extra_frameattrs)\\n return get_constellation(novel, short_name, constellation_list)\\n\\n # the simpler version below can be used when gh-issue #7028 is resolved\\n # return get_constellation(self, short_name, constellation_list)\\n\\n # WCS pixel to/from sky conversions\\n def to_pixel(self, wcs, origin=0, mode='all'):\\n \\\"\\\"\\\"\\n Convert this coordinate to pixel coordinates using a `~astropy.wcs.WCS`\\n object.\\n\\n Parameters\\n ----------\\n wcs : `~astropy.wcs.WCS`\\n The WCS to use for convert\\n origin : int\\n Whether to return 0 or 1-based pixel coordinates.\\n mode : 'all' or 'wcs'\\n Whether to do the transformation including distortions (``'all'``) or\\n only including only the core WCS transformation (``'wcs'``).\\n\\n Returns\\n -------\\n xp, yp : `numpy.ndarray`\\n The pixel coordinates\\n\\n See Also\\n --------\\n astropy.wcs.utils.skycoord_to_pixel : the implementation of this method\\n \\\"\\\"\\\"\\n from astropy.wcs.utils import skycoord_to_pixel\\n return skycoord_to_pixel(self, wcs=wcs, origin=origin, mode=mode)\\n\\n @classmethod\\n def from_pixel(cls, xp, yp, wcs, origin=0, mode='all'):\\n \\\"\\\"\\\"\\n Create a new `SkyCoord` from pixel coordinates using an\\n `~astropy.wcs.WCS` object.\\n\\n Parameters\\n ----------\\n xp, yp : float or `numpy.ndarray`\\n The coordinates to convert.\\n wcs : `~astropy.wcs.WCS`\\n The WCS to use for convert\\n origin : int\\n Whether to return 0 or 1-based pixel coordinates.\\n mode : 'all' or 'wcs'\\n Whether to do the transformation including distortions (``'all'``) or\\n only including only the core WCS transformation (``'wcs'``).\\n\\n Returns\\n -------\\n coord : an instance of this class\\n A new object with sky coordinates corresponding to the input ``xp``\\n and ``yp``.\\n\\n See Also\\n --------\\n to_pixel : to do the inverse operation\\n astropy.wcs.utils.pixel_to_skycoord : the implementation of this method\\n \\\"\\\"\\\"\\n from astropy.wcs.utils import pixel_to_skycoord\\n return pixel_to_skycoord(xp, yp, wcs=wcs, origin=origin, mode=mode, cls=cls)\\n\\n def contained_by(self, wcs, image=None, **kwargs):\\n \\\"\\\"\\\"\\n Determines if the SkyCoord is contained in the given wcs footprint.\\n\\n Parameters\\n ----------\\n wcs : `~astropy.wcs.WCS`\\n The coordinate to check if it is within the wcs coordinate.\\n image : array\\n Optional. The image associated with the wcs object that the cooordinate\\n is being checked against. If not given the naxis keywords will be used\\n to determine if the coordinate falls within the wcs footprint.\\n **kwargs :\\n Additional arguments to pass to `~astropy.coordinates.SkyCoord.to_pixel`\\n\\n Returns\\n -------\\n response : bool\\n True means the WCS footprint contains the coordinate, False means it does not.\\n \\\"\\\"\\\"\\n\\n if image is not None:\\n ymax, xmax = image.shape\\n else:\\n xmax, ymax = wcs._naxis\\n\\n import warnings\\n with warnings.catch_warnings():\\n # Suppress warnings since they just mean we didn't find the coordinate\\n warnings.simplefilter(\\\"ignore\\\")\\n try:\\n x, y = self.to_pixel(wcs, **kwargs)\\n except Exception:\\n return False\\n\\n return (x < xmax) & (x > 0) & (y < ymax) & (y > 0)\\n\\n def radial_velocity_correction(self, kind='barycentric', obstime=None,\\n location=None):\\n \\\"\\\"\\\"\\n Compute the correction required to convert a radial velocity at a given\\n time and place on the Earth's Surface to a barycentric or heliocentric\\n velocity.\\n\\n Parameters\\n ----------\\n kind : str\\n The kind of velocity correction. Must be 'barycentric' or\\n 'heliocentric'.\\n obstime : `~astropy.time.Time` or None, optional\\n The time at which to compute the correction. If `None`, the\\n ``obstime`` frame attribute on the `SkyCoord` will be used.\\n location : `~astropy.coordinates.EarthLocation` or None, optional\\n The observer location at which to compute the correction. If\\n `None`, the ``location`` frame attribute on the passed-in\\n ``obstime`` will be used, and if that is None, the ``location``\\n frame attribute on the `SkyCoord` will be used.\\n\\n Raises\\n ------\\n ValueError\\n If either ``obstime`` or ``location`` are passed in (not ``None``)\\n when the frame attribute is already set on this `SkyCoord`.\\n TypeError\\n If ``obstime`` or ``location`` aren't provided, either as arguments\\n or as frame attributes.\\n\\n Returns\\n -------\\n vcorr : `~astropy.units.Quantity` with velocity units\\n The correction with a positive sign. I.e., *add* this\\n to an observed radial velocity to get the barycentric (or\\n heliocentric) velocity. If m/s precision or better is needed,\\n see the notes below.\\n\\n Notes\\n -----\\n The barycentric correction is calculated to higher precision than the\\n heliocentric correction and includes additional physics (e.g time dilation).\\n Use barycentric corrections if m/s precision is required.\\n\\n The algorithm here is sufficient to perform corrections at the mm/s level, but\\n care is needed in application. The barycentric correction returned uses the optical\\n approximation v = z * c. Strictly speaking, the barycentric correction is\\n multiplicative and should be applied as::\\n\\n >>> from astropy.time import Time\\n >>> from astropy.coordinates import SkyCoord, EarthLocation\\n >>> from astropy.constants import c\\n >>> t = Time(56370.5, format='mjd', scale='utc')\\n >>> loc = EarthLocation('149d33m00.5s','-30d18m46.385s',236.87*u.m)\\n >>> sc = SkyCoord(1*u.deg, 2*u.deg)\\n >>> vcorr = sc.radial_velocity_correction(kind='barycentric', obstime=t, location=loc) # doctest: +REMOTE_DATA\\n >>> rv = rv + vcorr + rv * vcorr / c # doctest: +SKIP\\n\\n Also note that this method returns the correction velocity in the so-called\\n *optical convention*::\\n\\n >>> vcorr = zb * c # doctest: +SKIP\\n\\n where ``zb`` is the barycentric correction redshift as defined in section 3\\n of Wright & Eastman (2014). The application formula given above follows from their\\n equation (11) under assumption that the radial velocity ``rv`` has also been defined\\n using the same optical convention. Note, this can be regarded as a matter of\\n velocity definition and does not by itself imply any loss of accuracy, provided\\n sufficient care has been taken during interpretation of the results. If you need\\n the barycentric correction expressed as the full relativistic velocity (e.g., to provide\\n it as the input to another software which performs the application), the\\n following recipe can be used::\\n\\n >>> zb = vcorr / c # doctest: +REMOTE_DATA\\n >>> zb_plus_one_squared = (zb + 1) ** 2 # doctest: +REMOTE_DATA\\n >>> vcorr_rel = c * (zb_plus_one_squared - 1) / (zb_plus_one_squared + 1) # doctest: +REMOTE_DATA\\n\\n or alternatively using just equivalencies::\\n\\n >>> vcorr_rel = vcorr.to(u.Hz, u.doppler_optical(1*u.Hz)).to(vcorr.unit, u.doppler_relativistic(1*u.Hz)) # doctest: +REMOTE_DATA\\n\\n See also `~astropy.units.equivalencies.doppler_optical`,\\n `~astropy.units.equivalencies.doppler_radio`, and\\n `~astropy.units.equivalencies.doppler_relativistic` for more information on\\n the velocity conventions.\\n\\n The default is for this method to use the builtin ephemeris for\\n computing the sun and earth location. Other ephemerides can be chosen\\n by setting the `~astropy.coordinates.solar_system_ephemeris` variable,\\n either directly or via ``with`` statement. For example, to use the JPL\\n ephemeris, do::\\n\\n >>> from astropy.coordinates import solar_system_ephemeris\\n >>> sc = SkyCoord(1*u.deg, 2*u.deg)\\n >>> with solar_system_ephemeris.set('jpl'): # doctest: +REMOTE_DATA\\n ... rv += sc.radial_velocity_correction(obstime=t, location=loc) # doctest: +SKIP\\n\\n \\\"\\\"\\\"\\n # has to be here to prevent circular imports\\n from .solar_system import get_body_barycentric_posvel\\n\\n # location validation\\n timeloc = getattr(obstime, 'location', None)\\n if location is None:\\n if self.location is not None:\\n location = self.location\\n if timeloc is not None:\\n raise ValueError('`location` cannot be in both the '\\n 'passed-in `obstime` and this `SkyCoord` '\\n 'because it is ambiguous which is meant '\\n 'for the radial_velocity_correction.')\\n elif timeloc is not None:\\n location = timeloc\\n else:\\n raise TypeError('Must provide a `location` to '\\n 'radial_velocity_correction, either as a '\\n 'SkyCoord frame attribute, as an attribute on '\\n 'the passed in `obstime`, or in the method '\\n 'call.')\\n\\n elif self.location is not None or timeloc is not None:\\n raise ValueError('Cannot compute radial velocity correction if '\\n '`location` argument is passed in and there is '\\n 'also a `location` attribute on this SkyCoord or '\\n 'the passed-in `obstime`.')\\n\\n # obstime validation\\n coo_at_rv_obstime = self # assume we need no space motion for now\\n if obstime is None:\\n obstime = self.obstime\\n if obstime is None:\\n raise TypeError('Must provide an `obstime` to '\\n 'radial_velocity_correction, either as a '\\n 'SkyCoord frame attribute or in the method '\\n 'call.')\\n elif self.obstime is not None and self.frame.data.differentials:\\n # we do need space motion after all\\n coo_at_rv_obstime = self.apply_space_motion(obstime)\\n elif self.obstime is None:\\n # warn the user if the object has differentials set\\n if 's' in self.data.differentials:\\n warnings.warn(\\n \\\"SkyCoord has space motion, and therefore the specified \\\"\\n \\\"position of the SkyCoord may not be the same as \\\"\\n \\\"the `obstime` for the radial velocity measurement. \\\"\\n \\\"This may affect the rv correction at the order of km/s\\\"\\n \\\"for very high proper motions sources. If you wish to \\\"\\n \\\"apply space motion of the SkyCoord to correct for this\\\"\\n \\\"the `obstime` attribute of the SkyCoord must be set\\\",\\n AstropyUserWarning\\n )\\n\\n pos_earth, v_earth = get_body_barycentric_posvel('earth', obstime)\\n if kind == 'barycentric':\\n v_origin_to_earth = v_earth\\n elif kind == 'heliocentric':\\n v_sun = get_body_barycentric_posvel('sun', obstime)[1]\\n v_origin_to_earth = v_earth - v_sun\\n else:\\n raise ValueError(\\\"`kind` argument to radial_velocity_correction must \\\"\\n \\\"be 'barycentric' or 'heliocentric', but got \\\"\\n \\\"'{}'\\\".format(kind))\\n\\n gcrs_p, gcrs_v = location.get_gcrs_posvel(obstime)\\n # transforming to GCRS is not the correct thing to do here, since we don't want to\\n # include aberration (or light deflection)? Instead, only apply parallax if necessary\\n icrs_cart = coo_at_rv_obstime.icrs.cartesian\\n icrs_cart_novel = icrs_cart.without_differentials()\\n if self.data.__class__ is UnitSphericalRepresentation:\\n targcart = icrs_cart_novel\\n else:\\n # skycoord has distances so apply parallax\\n obs_icrs_cart = pos_earth + gcrs_p\\n targcart = icrs_cart_novel - obs_icrs_cart\\n targcart /= targcart.norm()\\n\\n if kind == 'barycentric':\\n beta_obs = (v_origin_to_earth + gcrs_v) / speed_of_light\\n gamma_obs = 1 / np.sqrt(1 - beta_obs.norm()**2)\\n gr = location.gravitational_redshift(obstime)\\n # barycentric redshift according to eq 28 in Wright & Eastmann (2014),\\n # neglecting Shapiro delay and effects of the star's own motion\\n zb = gamma_obs * (1 + beta_obs.dot(targcart)) / (1 + gr/speed_of_light)\\n # try and get terms corresponding to stellar motion.\\n if icrs_cart.differentials:\\n try:\\n ro = self.icrs.cartesian\\n beta_star = ro.differentials['s'].to_cartesian() / speed_of_light\\n # ICRS unit vector at coordinate epoch\\n ro = ro.without_differentials()\\n ro /= ro.norm()\\n zb *= (1 + beta_star.dot(ro)) / (1 + beta_star.dot(targcart))\\n except u.UnitConversionError:\\n warnings.warn(\\\"SkyCoord contains some velocity information, but not enough to \\\"\\n \\\"calculate the full space motion of the source, and so this has \\\"\\n \\\"been ignored for the purposes of calculating the radial velocity \\\"\\n \\\"correction. This can lead to errors on the order of metres/second.\\\",\\n AstropyUserWarning)\\n\\n zb = zb - 1\\n return zb * speed_of_light\\n else:\\n # do a simpler correction ignoring time dilation and gravitational redshift\\n # this is adequate since Heliocentric corrections shouldn't be used if\\n # cm/s precision is required.\\n return targcart.dot(v_origin_to_earth + gcrs_v)\\n\\n # Table interactions\\n @classmethod\\n def guess_from_table(cls, table, **coord_kwargs):\\n r\\\"\\\"\\\"\\n A convenience method to create and return a new `SkyCoord` from the data\\n in an astropy Table.\\n\\n This method matches table columns that start with the case-insensitive\\n names of the the components of the requested frames, if they are also\\n followed by a non-alphanumeric character. It will also match columns\\n that *end* with the component name if a non-alphanumeric character is\\n *before* it.\\n\\n For example, the first rule means columns with names like\\n ``'RA[J2000]'`` or ``'ra'`` will be interpreted as ``ra`` attributes for\\n `~astropy.coordinates.ICRS` frames, but ``'RAJ2000'`` or ``'radius'``\\n are *not*. Similarly, the second rule applied to the\\n `~astropy.coordinates.Galactic` frame means that a column named\\n ``'gal_l'`` will be used as the the ``l`` component, but ``gall`` or\\n ``'fill'`` will not.\\n\\n The definition of alphanumeric here is based on Unicode's definition\\n of alphanumeric, except without ``_`` (which is normally considered\\n alphanumeric). So for ASCII, this means the non-alphanumeric characters\\n are ``<space>_!\\\"#$%&'()*+,-./\\\\:;<=>?@[]^`{|}~``).\\n\\n Parameters\\n ----------\\n table : astropy.Table\\n The table to load data from.\\n coord_kwargs\\n Any additional keyword arguments are passed directly to this class's\\n constructor.\\n\\n Returns\\n -------\\n newsc : same as this class\\n The new `SkyCoord` (or subclass) object.\\n \\\"\\\"\\\"\\n _frame_cls, _frame_kwargs = _get_frame_without_data([], coord_kwargs)\\n frame = _frame_cls(**_frame_kwargs)\\n coord_kwargs['frame'] = coord_kwargs.get('frame', frame)\\n\\n comp_kwargs = {}\\n for comp_name in frame.representation_component_names:\\n # this matches things like 'ra[...]'' but *not* 'rad'.\\n # note that the \\\"_\\\" must be in there explicitly, because\\n # \\\"alphanumeric\\\" usually includes underscores.\\n starts_with_comp = comp_name + r'(\\\\W|\\\\b|_)'\\n # this part matches stuff like 'center_ra', but *not*\\n # 'aura'\\n ends_with_comp = r'.*(\\\\W|\\\\b|_)' + comp_name + r'\\\\b'\\n # the final regex ORs together the two patterns\\n rex = re.compile('(' + starts_with_comp + ')|(' + ends_with_comp + ')',\\n re.IGNORECASE | re.UNICODE)\\n\\n for col_name in table.colnames:\\n if rex.match(col_name):\\n if comp_name in comp_kwargs:\\n oldname = comp_kwargs[comp_name].name\\n msg = ('Found at least two matches for component \\\"{0}\\\"'\\n ': \\\"{1}\\\" and \\\"{2}\\\". Cannot continue with this '\\n 'ambiguity.')\\n raise ValueError(msg.format(comp_name, oldname, col_name))\\n comp_kwargs[comp_name] = table[col_name]\\n\\n for k, v in comp_kwargs.items():\\n if k in coord_kwargs:\\n raise ValueError('Found column \\\"{}\\\" in table, but it was '\\n 'already provided as \\\"{}\\\" keyword to '\\n 'guess_from_table function.'.format(v.name, k))\\n else:\\n coord_kwargs[k] = v\\n\\n return cls(**coord_kwargs)\\n\\n # Name resolve\\n @classmethod\\n def from_name(cls, name, frame='icrs', parse=False, cache=True):\\n \\\"\\\"\\\"\\n Given a name, query the CDS name resolver to attempt to retrieve\\n coordinate information for that object. The search database, sesame\\n url, and query timeout can be set through configuration items in\\n ``astropy.coordinates.name_resolve`` -- see docstring for\\n `~astropy.coordinates.get_icrs_coordinates` for more\\n information.\\n\\n Parameters\\n ----------\\n name : str\\n The name of the object to get coordinates for, e.g. ``'M42'``.\\n frame : str or `BaseCoordinateFrame` class or instance\\n The frame to transform the object to.\\n parse: bool\\n Whether to attempt extracting the coordinates from the name by\\n parsing with a regex. For objects catalog names that have\\n J-coordinates embedded in their names, e.g.,\\n 'CRTS SSS100805 J194428-420209', this may be much faster than a\\n Sesame query for the same object name. The coordinates extracted\\n in this way may differ from the database coordinates by a few\\n deci-arcseconds, so only use this option if you do not need\\n sub-arcsecond accuracy for coordinates.\\n cache : bool, optional\\n Determines whether to cache the results or not. To update or\\n overwrite an existing value, pass ``cache='update'``.\\n\\n Returns\\n -------\\n coord : SkyCoord\\n Instance of the SkyCoord class.\\n \\\"\\\"\\\"\\n\\n from .name_resolve import get_icrs_coordinates\\n\\n icrs_coord = get_icrs_coordinates(name, parse, cache=cache)\\n icrs_sky_coord = cls(icrs_coord)\\n if frame in ('icrs', icrs_coord.__class__):\\n return icrs_sky_coord\\n else:\\n return icrs_sky_coord.transform_to(frame)\\n\",\n \"# Licensed under a 3-clause BSD style license - see PYFITS.rst\\n\\nimport gzip\\nimport io\\nimport mmap\\nimport errno\\nimport os\\nimport pathlib\\nimport urllib.request\\nimport warnings\\nimport zipfile\\nfrom unittest.mock import patch\\n\\nimport pytest\\nimport numpy as np\\n\\nfrom . import FitsTestCase\\n\\nfrom astropy.io.fits.convenience import _getext\\nfrom astropy.io.fits.diff import FITSDiff\\nfrom astropy.io.fits.file import _File, GZIP_MAGIC\\n\\nfrom astropy.io import fits\\nfrom astropy.tests.helper import raises, catch_warnings, ignore_warnings\\nfrom astropy.utils.data import conf\\nfrom astropy.utils.exceptions import AstropyUserWarning\\nfrom astropy.utils import data\\n\\n# NOTE: Python can be built without bz2.\\ntry:\\n import bz2\\nexcept ImportError:\\n HAS_BZ2 = False\\nelse:\\n HAS_BZ2 = True\\n\\n\\nclass TestCore(FitsTestCase):\\n\\n @raises(OSError)\\n def test_missing_file(self):\\n fits.open(self.temp('does-not-exist.fits'))\\n\\n def test_naxisj_check(self):\\n with fits.open(self.data('o4sp040b0_raw.fits')) as hdulist:\\n hdulist[1].header['NAXIS3'] = 500\\n\\n assert 'NAXIS3' in hdulist[1].header\\n hdulist.verify('silentfix')\\n assert 'NAXIS3' not in hdulist[1].header\\n\\n def test_byteswap(self):\\n p = fits.PrimaryHDU()\\n l = fits.HDUList()\\n\\n n = np.zeros(3, dtype='i2')\\n n[0] = 1\\n n[1] = 60000\\n n[2] = 2\\n\\n c = fits.Column(name='foo', format='i2', bscale=1, bzero=32768,\\n array=n)\\n t = fits.BinTableHDU.from_columns([c])\\n\\n l.append(p)\\n l.append(t)\\n\\n l.writeto(self.temp('test.fits'), overwrite=True)\\n\\n with fits.open(self.temp('test.fits')) as p:\\n assert p[1].data[1]['foo'] == 60000.0\\n\\n def test_fits_file_path_object(self):\\n \\\"\\\"\\\"\\n Testing when fits file is passed as pathlib.Path object #4412.\\n \\\"\\\"\\\"\\n fpath = pathlib.Path(self.data('tdim.fits'))\\n with fits.open(fpath) as hdulist:\\n assert hdulist[0].filebytes() == 2880\\n assert hdulist[1].filebytes() == 5760\\n\\n with fits.open(self.data('tdim.fits')) as hdulist2:\\n assert FITSDiff(hdulist2, hdulist).identical is True\\n\\n def test_fits_file_bytes_object(self):\\n \\\"\\\"\\\"\\n Testing when fits file is passed as bytes.\\n \\\"\\\"\\\"\\n with fits.open(self.data('tdim.fits').encode()) as hdulist:\\n assert hdulist[0].filebytes() == 2880\\n assert hdulist[1].filebytes() == 5760\\n\\n with fits.open(self.data('tdim.fits')) as hdulist2:\\n assert FITSDiff(hdulist2, hdulist).identical is True\\n\\n def test_add_del_columns(self):\\n p = fits.ColDefs([])\\n p.add_col(fits.Column(name='FOO', format='3J'))\\n p.add_col(fits.Column(name='BAR', format='1I'))\\n assert p.names == ['FOO', 'BAR']\\n p.del_col('FOO')\\n assert p.names == ['BAR']\\n\\n def test_add_del_columns2(self):\\n hdulist = fits.open(self.data('tb.fits'))\\n table = hdulist[1]\\n assert table.data.dtype.names == ('c1', 'c2', 'c3', 'c4')\\n assert table.columns.names == ['c1', 'c2', 'c3', 'c4']\\n table.columns.del_col('c1')\\n assert table.data.dtype.names == ('c2', 'c3', 'c4')\\n assert table.columns.names == ['c2', 'c3', 'c4']\\n\\n table.columns.del_col('c3')\\n assert table.data.dtype.names == ('c2', 'c4')\\n assert table.columns.names == ['c2', 'c4']\\n\\n table.columns.add_col(fits.Column('foo', '3J'))\\n assert table.data.dtype.names == ('c2', 'c4', 'foo')\\n assert table.columns.names == ['c2', 'c4', 'foo']\\n\\n hdulist.writeto(self.temp('test.fits'), overwrite=True)\\n with ignore_warnings():\\n # TODO: The warning raised by this test is actually indication of a\\n # bug and should *not* be ignored. But as it is a known issue we\\n # hide it for now. See\\n # https://github.com/spacetelescope/PyFITS/issues/44\\n with fits.open(self.temp('test.fits')) as hdulist:\\n table = hdulist[1]\\n assert table.data.dtype.names == ('c2', 'c4', 'foo')\\n assert table.columns.names == ['c2', 'c4', 'foo']\\n\\n def test_update_header_card(self):\\n \\\"\\\"\\\"A very basic test for the Header.update method--I'd like to add a\\n few more cases to this at some point.\\n \\\"\\\"\\\"\\n\\n header = fits.Header()\\n comment = 'number of bits per data pixel'\\n header['BITPIX'] = (16, comment)\\n assert 'BITPIX' in header\\n assert header['BITPIX'] == 16\\n assert header.comments['BITPIX'] == comment\\n\\n header.update(BITPIX=32)\\n assert header['BITPIX'] == 32\\n assert header.comments['BITPIX'] == ''\\n\\n def test_set_card_value(self):\\n \\\"\\\"\\\"Similar to test_update_header_card(), but tests the the\\n `header['FOO'] = 'bar'` method of updating card values.\\n \\\"\\\"\\\"\\n\\n header = fits.Header()\\n comment = 'number of bits per data pixel'\\n card = fits.Card.fromstring(f'BITPIX = 32 / {comment}')\\n header.append(card)\\n\\n header['BITPIX'] = 32\\n\\n assert 'BITPIX' in header\\n assert header['BITPIX'] == 32\\n assert header.cards[0].keyword == 'BITPIX'\\n assert header.cards[0].value == 32\\n assert header.cards[0].comment == comment\\n\\n def test_uint(self):\\n filename = self.data('o4sp040b0_raw.fits')\\n with fits.open(filename, uint=False) as hdulist_f:\\n with fits.open(filename, uint=True) as hdulist_i:\\n assert hdulist_f[1].data.dtype == np.float32\\n assert hdulist_i[1].data.dtype == np.uint16\\n assert np.all(hdulist_f[1].data == hdulist_i[1].data)\\n\\n def test_fix_missing_card_append(self):\\n hdu = fits.ImageHDU()\\n errs = hdu.req_cards('TESTKW', None, None, 'foo', 'silentfix', [])\\n assert len(errs) == 1\\n assert 'TESTKW' in hdu.header\\n assert hdu.header['TESTKW'] == 'foo'\\n assert hdu.header.cards[-1].keyword == 'TESTKW'\\n\\n def test_fix_invalid_keyword_value(self):\\n hdu = fits.ImageHDU()\\n hdu.header['TESTKW'] = 'foo'\\n errs = hdu.req_cards('TESTKW', None,\\n lambda v: v == 'foo', 'foo', 'ignore', [])\\n assert len(errs) == 0\\n\\n # Now try a test that will fail, and ensure that an error will be\\n # raised in 'exception' mode\\n errs = hdu.req_cards('TESTKW', None, lambda v: v == 'bar', 'bar',\\n 'exception', [])\\n assert len(errs) == 1\\n assert errs[0][1] == \\\"'TESTKW' card has invalid value 'foo'.\\\"\\n\\n # See if fixing will work\\n hdu.req_cards('TESTKW', None, lambda v: v == 'bar', 'bar', 'silentfix',\\n [])\\n assert hdu.header['TESTKW'] == 'bar'\\n\\n @raises(fits.VerifyError)\\n def test_unfixable_missing_card(self):\\n class TestHDU(fits.hdu.base.NonstandardExtHDU):\\n def _verify(self, option='warn'):\\n errs = super()._verify(option)\\n hdu.req_cards('TESTKW', None, None, None, 'fix', errs)\\n return errs\\n\\n @classmethod\\n def match_header(cls, header):\\n # Since creating this HDU class adds it to the registry we\\n # don't want the file reader to possibly think any actual\\n # HDU from a file should be handled by this class\\n return False\\n\\n hdu = TestHDU(header=fits.Header())\\n hdu.verify('fix')\\n\\n @raises(fits.VerifyError)\\n def test_exception_on_verification_error(self):\\n hdu = fits.ImageHDU()\\n del hdu.header['XTENSION']\\n hdu.verify('exception')\\n\\n def test_ignore_verification_error(self):\\n hdu = fits.ImageHDU()\\n # The default here would be to issue a warning; ensure that no warnings\\n # or exceptions are raised\\n with catch_warnings():\\n warnings.simplefilter('error')\\n del hdu.header['NAXIS']\\n try:\\n hdu.verify('ignore')\\n except Exception as exc:\\n self.fail('An exception occurred when the verification error '\\n 'should have been ignored: {}'.format(exc))\\n # Make sure the error wasn't fixed either, silently or otherwise\\n assert 'NAXIS' not in hdu.header\\n\\n @raises(ValueError)\\n def test_unrecognized_verify_option(self):\\n hdu = fits.ImageHDU()\\n hdu.verify('foobarbaz')\\n\\n def test_errlist_basic(self):\\n # Just some tests to make sure that _ErrList is setup correctly.\\n # No arguments\\n error_list = fits.verify._ErrList()\\n assert error_list == []\\n # Some contents - this is not actually working, it just makes sure they\\n # are kept.\\n error_list = fits.verify._ErrList([1, 2, 3])\\n assert error_list == [1, 2, 3]\\n\\n def test_combined_verify_options(self):\\n \\\"\\\"\\\"\\n Test verify options like fix+ignore.\\n \\\"\\\"\\\"\\n\\n def make_invalid_hdu():\\n hdu = fits.ImageHDU()\\n # Add one keyword to the header that contains a fixable defect, and one\\n # with an unfixable defect\\n c1 = fits.Card.fromstring(\\\"test = ' test'\\\")\\n c2 = fits.Card.fromstring(\\\"P.I. = ' Hubble'\\\")\\n hdu.header.append(c1)\\n hdu.header.append(c2)\\n return hdu\\n\\n # silentfix+ignore should be completely silent\\n hdu = make_invalid_hdu()\\n with catch_warnings():\\n warnings.simplefilter('error')\\n try:\\n hdu.verify('silentfix+ignore')\\n except Exception as exc:\\n self.fail('An exception occurred when the verification error '\\n 'should have been ignored: {}'.format(exc))\\n\\n # silentfix+warn should be quiet about the fixed HDU and only warn\\n # about the unfixable one\\n hdu = make_invalid_hdu()\\n with catch_warnings() as w:\\n hdu.verify('silentfix+warn')\\n assert len(w) == 4\\n assert 'Illegal keyword name' in str(w[2].message)\\n\\n # silentfix+exception should only mention the unfixable error in the\\n # exception\\n hdu = make_invalid_hdu()\\n with pytest.raises(fits.VerifyError, match=r'Illegal keyword name') as excinfo:\\n hdu.verify('silentfix+exception')\\n assert 'not upper case' not in str(excinfo.value)\\n\\n # fix+ignore is not too useful, but it should warn about the fixed\\n # problems while saying nothing about the unfixable problems\\n hdu = make_invalid_hdu()\\n with catch_warnings() as w:\\n hdu.verify('fix+ignore')\\n assert len(w) == 4\\n assert 'not upper case' in str(w[2].message)\\n\\n # fix+warn\\n hdu = make_invalid_hdu()\\n with catch_warnings() as w:\\n hdu.verify('fix+warn')\\n assert len(w) == 6\\n assert 'not upper case' in str(w[2].message)\\n assert 'Illegal keyword name' in str(w[4].message)\\n\\n # fix+exception\\n hdu = make_invalid_hdu()\\n with pytest.raises(fits.VerifyError, match=r'Illegal keyword name') as excinfo:\\n hdu.verify('fix+exception')\\n assert 'not upper case' in str(excinfo.value)\\n\\n def test_getext(self):\\n \\\"\\\"\\\"\\n Test the various different ways of specifying an extension header in\\n the convenience functions.\\n \\\"\\\"\\\"\\n filename = self.data('test0.fits')\\n\\n hl, ext = _getext(filename, 'readonly', 1)\\n assert ext == 1\\n hl.close()\\n\\n pytest.raises(ValueError, _getext, filename, 'readonly',\\n 1, 2)\\n pytest.raises(ValueError, _getext, filename, 'readonly',\\n (1, 2))\\n pytest.raises(ValueError, _getext, filename, 'readonly',\\n 'sci', 'sci')\\n pytest.raises(TypeError, _getext, filename, 'readonly',\\n 1, 2, 3)\\n\\n hl, ext = _getext(filename, 'readonly', ext=1)\\n assert ext == 1\\n hl.close()\\n\\n hl, ext = _getext(filename, 'readonly', ext=('sci', 2))\\n assert ext == ('sci', 2)\\n hl.close()\\n\\n pytest.raises(TypeError, _getext, filename, 'readonly',\\n 1, ext=('sci', 2), extver=3)\\n pytest.raises(TypeError, _getext, filename, 'readonly',\\n ext=('sci', 2), extver=3)\\n\\n hl, ext = _getext(filename, 'readonly', 'sci')\\n assert ext == ('sci', 1)\\n hl.close()\\n\\n hl, ext = _getext(filename, 'readonly', 'sci', 1)\\n assert ext == ('sci', 1)\\n hl.close()\\n\\n hl, ext = _getext(filename, 'readonly', ('sci', 1))\\n assert ext == ('sci', 1)\\n hl.close()\\n\\n hl, ext = _getext(filename, 'readonly', 'sci',\\n extver=1, do_not_scale_image_data=True)\\n assert ext == ('sci', 1)\\n hl.close()\\n\\n pytest.raises(TypeError, _getext, filename, 'readonly',\\n 'sci', ext=1)\\n pytest.raises(TypeError, _getext, filename, 'readonly',\\n 'sci', 1, extver=2)\\n\\n hl, ext = _getext(filename, 'readonly', extname='sci')\\n assert ext == ('sci', 1)\\n hl.close()\\n\\n hl, ext = _getext(filename, 'readonly', extname='sci',\\n extver=1)\\n assert ext == ('sci', 1)\\n hl.close()\\n\\n pytest.raises(TypeError, _getext, filename, 'readonly',\\n extver=1)\\n\\n def test_extension_name_case_sensitive(self):\\n \\\"\\\"\\\"\\n Tests that setting fits.conf.extension_name_case_sensitive at\\n runtime works.\\n \\\"\\\"\\\"\\n\\n hdu = fits.ImageHDU()\\n hdu.name = 'sCi'\\n assert hdu.name == 'SCI'\\n assert hdu.header['EXTNAME'] == 'SCI'\\n\\n with fits.conf.set_temp('extension_name_case_sensitive', True):\\n hdu = fits.ImageHDU()\\n hdu.name = 'sCi'\\n assert hdu.name == 'sCi'\\n assert hdu.header['EXTNAME'] == 'sCi'\\n\\n hdu.name = 'sCi'\\n assert hdu.name == 'SCI'\\n assert hdu.header['EXTNAME'] == 'SCI'\\n\\n def test_hdu_fromstring(self):\\n \\\"\\\"\\\"\\n Tests creating a fully-formed HDU object from a string containing the\\n bytes of the HDU.\\n \\\"\\\"\\\"\\n infile = self.data('test0.fits')\\n outfile = self.temp('test.fits')\\n\\n with open(infile, 'rb') as fin:\\n dat = fin.read()\\n\\n offset = 0\\n with fits.open(infile) as hdul:\\n hdulen = hdul[0]._data_offset + hdul[0]._data_size\\n hdu = fits.PrimaryHDU.fromstring(dat[:hdulen])\\n assert isinstance(hdu, fits.PrimaryHDU)\\n assert hdul[0].header == hdu.header\\n assert hdu.data is None\\n\\n hdu.header['TEST'] = 'TEST'\\n hdu.writeto(outfile)\\n with fits.open(outfile) as hdul:\\n assert isinstance(hdu, fits.PrimaryHDU)\\n assert hdul[0].header[:-1] == hdu.header[:-1]\\n assert hdul[0].header['TEST'] == 'TEST'\\n assert hdu.data is None\\n\\n with fits.open(infile)as hdul:\\n for ext_hdu in hdul[1:]:\\n offset += hdulen\\n hdulen = len(str(ext_hdu.header)) + ext_hdu._data_size\\n hdu = fits.ImageHDU.fromstring(dat[offset:offset + hdulen])\\n assert isinstance(hdu, fits.ImageHDU)\\n assert ext_hdu.header == hdu.header\\n assert (ext_hdu.data == hdu.data).all()\\n\\n def test_nonstandard_hdu(self):\\n \\\"\\\"\\\"\\n Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/157\\n\\n Tests that \\\"Nonstandard\\\" HDUs with SIMPLE = F are read and written\\n without prepending a superfluous and unwanted standard primary HDU.\\n \\\"\\\"\\\"\\n\\n data = np.arange(100, dtype=np.uint8)\\n hdu = fits.PrimaryHDU(data=data)\\n hdu.header['SIMPLE'] = False\\n hdu.writeto(self.temp('test.fits'))\\n\\n info = [(0, '', 1, 'NonstandardHDU', 5, (), '', '')]\\n with fits.open(self.temp('test.fits')) as hdul:\\n assert hdul.info(output=False) == info\\n # NonstandardHDUs just treat the data as an unspecified array of\\n # bytes. The first 100 bytes should match the original data we\\n # passed in...the rest should be zeros padding out the rest of the\\n # FITS block\\n assert (hdul[0].data[:100] == data).all()\\n assert (hdul[0].data[100:] == 0).all()\\n\\n def test_extname(self):\\n \\\"\\\"\\\"Test getting/setting the EXTNAME of an HDU.\\\"\\\"\\\"\\n\\n h1 = fits.PrimaryHDU()\\n assert h1.name == 'PRIMARY'\\n # Normally a PRIMARY HDU should not have an EXTNAME, though it should\\n # have a default .name attribute\\n assert 'EXTNAME' not in h1.header\\n\\n # The current version of the FITS standard does allow PRIMARY HDUs to\\n # have an EXTNAME, however.\\n h1.name = 'NOTREAL'\\n assert h1.name == 'NOTREAL'\\n assert h1.header.get('EXTNAME') == 'NOTREAL'\\n\\n # Updating the EXTNAME in the header should update the .name\\n h1.header['EXTNAME'] = 'TOOREAL'\\n assert h1.name == 'TOOREAL'\\n\\n # If we delete an EXTNAME keyword from a PRIMARY HDU it should go back\\n # to the default\\n del h1.header['EXTNAME']\\n assert h1.name == 'PRIMARY'\\n\\n # For extension HDUs the situation is a bit simpler:\\n h2 = fits.ImageHDU()\\n assert h2.name == ''\\n assert 'EXTNAME' not in h2.header\\n h2.name = 'HELLO'\\n assert h2.name == 'HELLO'\\n assert h2.header.get('EXTNAME') == 'HELLO'\\n h2.header['EXTNAME'] = 'GOODBYE'\\n assert h2.name == 'GOODBYE'\\n\\n def test_extver_extlevel(self):\\n \\\"\\\"\\\"Test getting/setting the EXTVER and EXTLEVEL of and HDU.\\\"\\\"\\\"\\n\\n # EXTVER and EXTNAME work exactly the same; their semantics are, for\\n # now, to be inferred by the user. Although they should never be less\\n # than 1, the standard does not explicitly forbid any value so long as\\n # it's an integer\\n h1 = fits.PrimaryHDU()\\n assert h1.ver == 1\\n assert h1.level == 1\\n assert 'EXTVER' not in h1.header\\n assert 'EXTLEVEL' not in h1.header\\n\\n h1.ver = 2\\n assert h1.header.get('EXTVER') == 2\\n h1.header['EXTVER'] = 3\\n assert h1.ver == 3\\n del h1.header['EXTVER']\\n h1.ver == 1\\n\\n h1.level = 2\\n assert h1.header.get('EXTLEVEL') == 2\\n h1.header['EXTLEVEL'] = 3\\n assert h1.level == 3\\n del h1.header['EXTLEVEL']\\n assert h1.level == 1\\n\\n pytest.raises(TypeError, setattr, h1, 'ver', 'FOO')\\n pytest.raises(TypeError, setattr, h1, 'level', 'BAR')\\n\\n def test_consecutive_writeto(self):\\n \\\"\\\"\\\"\\n Regression test for an issue where calling writeto twice on the same\\n HDUList could write a corrupted file.\\n\\n https://github.com/spacetelescope/PyFITS/issues/40 is actually a\\n particular instance of this problem, though isn't unique to sys.stdout.\\n \\\"\\\"\\\"\\n\\n with fits.open(self.data('test0.fits')) as hdul1:\\n # Add a bunch of header keywords so that the data will be forced to\\n # new offsets within the file:\\n for idx in range(40):\\n hdul1[1].header[f'TEST{idx}'] = 'test'\\n\\n hdul1.writeto(self.temp('test1.fits'))\\n hdul1.writeto(self.temp('test2.fits'))\\n\\n # Open a second handle to the original file and compare it to hdul1\\n # (We only compare part of the one header that was modified)\\n # Compare also with the second writeto output\\n with fits.open(self.data('test0.fits')) as hdul2:\\n with fits.open(self.temp('test2.fits')) as hdul3:\\n for hdul in (hdul1, hdul3):\\n for idx, hdus in enumerate(zip(hdul2, hdul)):\\n hdu2, hdu = hdus\\n if idx != 1:\\n assert hdu.header == hdu2.header\\n else:\\n assert (hdu2.header ==\\n hdu.header[:len(hdu2.header)])\\n assert np.all(hdu.data == hdu2.data)\\n\\n\\nclass TestConvenienceFunctions(FitsTestCase):\\n def test_writeto(self):\\n \\\"\\\"\\\"\\n Simple test for writing a trivial header and some data to a file\\n with the `writeto()` convenience function.\\n \\\"\\\"\\\"\\n filename = self.temp('array.fits')\\n data = np.zeros((100, 100))\\n header = fits.Header()\\n fits.writeto(filename, data, header=header, overwrite=True)\\n with fits.open(filename) as hdul:\\n assert len(hdul) == 1\\n assert (data == hdul[0].data).all()\\n\\n def test_writeto_2(self):\\n \\\"\\\"\\\"\\n Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/107\\n\\n Test of `writeto()` with a trivial header containing a single keyword.\\n \\\"\\\"\\\"\\n filename = self.temp('array.fits')\\n data = np.zeros((100, 100))\\n header = fits.Header()\\n header.set('CRPIX1', 1.)\\n fits.writeto(filename, data, header=header,\\n overwrite=True, output_verify='silentfix')\\n with fits.open(filename) as hdul:\\n assert len(hdul) == 1\\n assert (data == hdul[0].data).all()\\n assert 'CRPIX1' in hdul[0].header\\n assert hdul[0].header['CRPIX1'] == 1.0\\n\\n\\nclass TestFileFunctions(FitsTestCase):\\n \\\"\\\"\\\"\\n Tests various basic I/O operations, specifically in the\\n astropy.io.fits.file._File class.\\n \\\"\\\"\\\"\\n\\n def test_open_nonexistent(self):\\n \\\"\\\"\\\"Test that trying to open a non-existent file results in an\\n OSError (and not some other arbitrary exception).\\n \\\"\\\"\\\"\\n\\n with pytest.raises(OSError, match=r'No such file or directory'):\\n fits.open(self.temp('foobar.fits'))\\n\\n # But opening in ostream or append mode should be okay, since they\\n # allow writing new files\\n for mode in ('ostream', 'append'):\\n with fits.open(self.temp('foobar.fits'), mode=mode) as _:\\n pass\\n\\n assert os.path.exists(self.temp('foobar.fits'))\\n os.remove(self.temp('foobar.fits'))\\n\\n def test_open_file_handle(self):\\n # Make sure we can open a FITS file from an open file handle\\n with open(self.data('test0.fits'), 'rb') as handle:\\n with fits.open(handle) as _:\\n pass\\n\\n with open(self.temp('temp.fits'), 'wb') as handle:\\n with fits.open(handle, mode='ostream') as _:\\n pass\\n\\n # Opening without explicitly specifying binary mode should fail\\n with pytest.raises(ValueError):\\n with open(self.data('test0.fits')) as handle:\\n with fits.open(handle) as _:\\n pass\\n\\n # All of these read modes should fail\\n for mode in ['r', 'rt']:\\n with pytest.raises(ValueError):\\n with open(self.data('test0.fits'), mode=mode) as handle:\\n with fits.open(handle) as _:\\n pass\\n\\n # These update or write modes should fail as well\\n for mode in ['w', 'wt', 'w+', 'wt+', 'r+', 'rt+',\\n 'a', 'at', 'a+', 'at+']:\\n with pytest.raises(ValueError):\\n with open(self.temp('temp.fits'), mode=mode) as handle:\\n with fits.open(handle) as _:\\n pass\\n\\n def test_fits_file_handle_mode_combo(self):\\n # This should work fine since no mode is given\\n with open(self.data('test0.fits'), 'rb') as handle:\\n with fits.open(handle) as _:\\n pass\\n\\n # This should work fine since the modes are compatible\\n with open(self.data('test0.fits'), 'rb') as handle:\\n with fits.open(handle, mode='readonly') as _:\\n pass\\n\\n # This should not work since the modes conflict\\n with pytest.raises(ValueError):\\n with open(self.data('test0.fits'), 'rb') as handle:\\n with fits.open(handle, mode='ostream') as _:\\n pass\\n\\n def test_open_from_url(self):\\n file_url = 'file:///' + self.data('test0.fits').lstrip('/')\\n with urllib.request.urlopen(file_url) as urlobj:\\n with fits.open(urlobj) as _:\\n pass\\n\\n # It will not be possible to write to a file that is from a URL object\\n for mode in ('ostream', 'append', 'update'):\\n with pytest.raises(ValueError):\\n with urllib.request.urlopen(file_url) as urlobj:\\n with fits.open(urlobj, mode=mode) as _:\\n pass\\n\\n @pytest.mark.remote_data(source='astropy')\\n def test_open_from_remote_url(self):\\n for dataurl in (conf.dataurl, conf.dataurl_mirror):\\n remote_url = '{}/{}'.format(dataurl, 'allsky/allsky_rosat.fits')\\n try:\\n with urllib.request.urlopen(remote_url) as urlobj:\\n with fits.open(urlobj) as fits_handle:\\n assert len(fits_handle) == 1\\n\\n for mode in ('ostream', 'append', 'update'):\\n with pytest.raises(ValueError):\\n with urllib.request.urlopen(remote_url) as urlobj:\\n with fits.open(urlobj, mode=mode) as fits_handle:\\n assert len(fits_handle) == 1\\n except (urllib.error.HTTPError, urllib.error.URLError):\\n continue\\n else:\\n break\\n else:\\n raise Exception(\\\"Could not download file\\\")\\n\\n def test_open_gzipped(self):\\n gzip_file = self._make_gzip_file()\\n with ignore_warnings():\\n with fits.open(gzip_file) as fits_handle:\\n assert fits_handle._file.compression == 'gzip'\\n assert len(fits_handle) == 5\\n with fits.open(gzip.GzipFile(gzip_file)) as fits_handle:\\n assert fits_handle._file.compression == 'gzip'\\n assert len(fits_handle) == 5\\n\\n def test_open_gzipped_from_handle(self):\\n with open(self._make_gzip_file(), 'rb') as handle:\\n with fits.open(handle) as fits_handle:\\n assert fits_handle._file.compression == 'gzip'\\n\\n def test_detect_gzipped(self):\\n \\\"\\\"\\\"Test detection of a gzip file when the extension is not .gz.\\\"\\\"\\\"\\n with ignore_warnings():\\n with fits.open(self._make_gzip_file('test0.fz')) as fits_handle:\\n assert fits_handle._file.compression == 'gzip'\\n assert len(fits_handle) == 5\\n\\n def test_writeto_append_mode_gzip(self):\\n \\\"\\\"\\\"Regression test for\\n https://github.com/spacetelescope/PyFITS/issues/33\\n\\n Check that a new GzipFile opened in append mode can be used to write\\n out a new FITS file.\\n \\\"\\\"\\\"\\n\\n # Note: when opening a GzipFile the 'b+' is superfluous, but this was\\n # still how the original test case looked\\n # Note: with statement not supported on GzipFile in older Python\\n # versions\\n fileobj = gzip.GzipFile(self.temp('test.fits.gz'), 'ab+')\\n h = fits.PrimaryHDU()\\n try:\\n h.writeto(fileobj)\\n finally:\\n fileobj.close()\\n\\n with fits.open(self.temp('test.fits.gz')) as hdul:\\n assert hdul[0].header == h.header\\n\\n def test_fits_update_mode_gzip(self):\\n \\\"\\\"\\\"Test updating a GZipped FITS file\\\"\\\"\\\"\\n\\n with fits.open(self._make_gzip_file('update.gz'), mode='update') as fits_handle:\\n hdu = fits.ImageHDU(data=[x for x in range(100)])\\n fits_handle.append(hdu)\\n\\n with fits.open(self.temp('update.gz')) as new_handle:\\n assert len(new_handle) == 6\\n assert (new_handle[-1].data == [x for x in range(100)]).all()\\n\\n def test_fits_append_mode_gzip(self):\\n \\\"\\\"\\\"Make sure that attempting to open an existing GZipped FITS file in\\n 'append' mode raises an error\\\"\\\"\\\"\\n\\n with pytest.raises(OSError):\\n with fits.open(self._make_gzip_file('append.gz'), mode='append') as _:\\n pass\\n\\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\\n def test_open_bzipped(self):\\n bzip_file = self._make_bzip2_file()\\n with ignore_warnings():\\n with fits.open(bzip_file) as fits_handle:\\n assert fits_handle._file.compression == 'bzip2'\\n assert len(fits_handle) == 5\\n\\n with fits.open(bz2.BZ2File(bzip_file)) as fits_handle:\\n assert fits_handle._file.compression == 'bzip2'\\n assert len(fits_handle) == 5\\n\\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\\n def test_open_bzipped_from_handle(self):\\n with open(self._make_bzip2_file(), 'rb') as handle:\\n with fits.open(handle) as fits_handle:\\n assert fits_handle._file.compression == 'bzip2'\\n assert len(fits_handle) == 5\\n\\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\\n def test_detect_bzipped(self):\\n \\\"\\\"\\\"Test detection of a bzip2 file when the extension is not .bz2.\\\"\\\"\\\"\\n with ignore_warnings():\\n with fits.open(self._make_bzip2_file('test0.xx')) as fits_handle:\\n assert fits_handle._file.compression == 'bzip2'\\n assert len(fits_handle) == 5\\n\\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\\n def test_writeto_bzip2_fileobj(self):\\n \\\"\\\"\\\"Test writing to a bz2.BZ2File file like object\\\"\\\"\\\"\\n fileobj = bz2.BZ2File(self.temp('test.fits.bz2'), 'w')\\n h = fits.PrimaryHDU()\\n try:\\n h.writeto(fileobj)\\n finally:\\n fileobj.close()\\n\\n with fits.open(self.temp('test.fits.bz2')) as hdul:\\n assert hdul[0].header == h.header\\n\\n @pytest.mark.skipif(not HAS_BZ2, reason='Python built without bz2 module')\\n def test_writeto_bzip2_filename(self):\\n \\\"\\\"\\\"Test writing to a bzip2 file by name\\\"\\\"\\\"\\n filename = self.temp('testname.fits.bz2')\\n h = fits.PrimaryHDU()\\n h.writeto(filename)\\n\\n with fits.open(self.temp('testname.fits.bz2')) as hdul:\\n assert hdul[0].header == h.header\\n\\n def test_open_zipped(self):\\n zip_file = self._make_zip_file()\\n with ignore_warnings():\\n with fits.open(zip_file) as fits_handle:\\n assert fits_handle._file.compression == 'zip'\\n assert len(fits_handle) == 5\\n with fits.open(zipfile.ZipFile(zip_file)) as fits_handle:\\n assert fits_handle._file.compression == 'zip'\\n assert len(fits_handle) == 5\\n\\n def test_open_zipped_from_handle(self):\\n with open(self._make_zip_file(), 'rb') as handle:\\n with fits.open(handle) as fits_handle:\\n assert fits_handle._file.compression == 'zip'\\n assert len(fits_handle) == 5\\n\\n def test_detect_zipped(self):\\n \\\"\\\"\\\"Test detection of a zip file when the extension is not .zip.\\\"\\\"\\\"\\n\\n zf = self._make_zip_file(filename='test0.fz')\\n with ignore_warnings():\\n assert len(fits.open(zf)) == 5\\n\\n def test_open_zipped_writeable(self):\\n \\\"\\\"\\\"Opening zipped files in a writeable mode should fail.\\\"\\\"\\\"\\n\\n zf = self._make_zip_file()\\n pytest.raises(OSError, fits.open, zf, 'update')\\n pytest.raises(OSError, fits.open, zf, 'append')\\n\\n zf = zipfile.ZipFile(zf, 'a')\\n pytest.raises(OSError, fits.open, zf, 'update')\\n pytest.raises(OSError, fits.open, zf, 'append')\\n\\n def test_read_open_astropy_gzip_file(self):\\n \\\"\\\"\\\"\\n Regression test for https://github.com/astropy/astropy/issues/2774\\n\\n This tests reading from a ``GzipFile`` object from Astropy's\\n compatibility copy of the ``gzip`` module.\\n \\\"\\\"\\\"\\n gf = gzip.GzipFile(self._make_gzip_file())\\n try:\\n assert len(fits.open(gf)) == 5\\n finally:\\n gf.close()\\n\\n @raises(OSError)\\n def test_open_multiple_member_zipfile(self):\\n \\\"\\\"\\\"\\n Opening zip files containing more than one member files should fail\\n as there's no obvious way to specify which file is the FITS file to\\n read.\\n \\\"\\\"\\\"\\n\\n zfile = zipfile.ZipFile(self.temp('test0.zip'), 'w')\\n zfile.write(self.data('test0.fits'))\\n zfile.writestr('foo', 'bar')\\n zfile.close()\\n\\n fits.open(zfile.filename)\\n\\n def test_read_open_file(self):\\n \\\"\\\"\\\"Read from an existing file object.\\\"\\\"\\\"\\n\\n with open(self.data('test0.fits'), 'rb') as f:\\n assert len(fits.open(f)) == 5\\n\\n def test_read_closed_file(self):\\n \\\"\\\"\\\"Read from an existing file object that's been closed.\\\"\\\"\\\"\\n\\n f = open(self.data('test0.fits'), 'rb')\\n f.close()\\n with fits.open(f) as f2:\\n assert len(f2) == 5\\n\\n def test_read_open_gzip_file(self):\\n \\\"\\\"\\\"Read from an open gzip file object.\\\"\\\"\\\"\\n\\n gf = gzip.GzipFile(self._make_gzip_file())\\n try:\\n assert len(fits.open(gf)) == 5\\n finally:\\n gf.close()\\n\\n def test_open_gzip_file_for_writing(self):\\n \\\"\\\"\\\"Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/195.\\\"\\\"\\\"\\n\\n gf = self._make_gzip_file()\\n with fits.open(gf, mode='update') as h:\\n h[0].header['EXPFLAG'] = 'ABNORMAL'\\n h[1].data[0, 0] = 1\\n with fits.open(gf) as h:\\n # Just to make sur ethe update worked; if updates work\\n # normal writes should work too...\\n assert h[0].header['EXPFLAG'] == 'ABNORMAL'\\n assert h[1].data[0, 0] == 1\\n\\n def test_write_read_gzip_file(self):\\n \\\"\\\"\\\"\\n Regression test for https://github.com/astropy/astropy/issues/2794\\n\\n Ensure files written through gzip are readable.\\n \\\"\\\"\\\"\\n\\n data = np.arange(100)\\n hdu = fits.PrimaryHDU(data=data)\\n hdu.writeto(self.temp('test.fits.gz'))\\n\\n with open(self.temp('test.fits.gz'), 'rb') as f:\\n assert f.read(3) == GZIP_MAGIC\\n\\n with fits.open(self.temp('test.fits.gz')) as hdul:\\n assert np.all(hdul[0].data == data)\\n\\n def test_read_file_like_object(self):\\n \\\"\\\"\\\"Test reading a FITS file from a file-like object.\\\"\\\"\\\"\\n\\n filelike = io.BytesIO()\\n with open(self.data('test0.fits'), 'rb') as f:\\n filelike.write(f.read())\\n filelike.seek(0)\\n with ignore_warnings():\\n assert len(fits.open(filelike)) == 5\\n\\n def test_updated_file_permissions(self):\\n \\\"\\\"\\\"\\n Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/79\\n\\n Tests that when a FITS file is modified in update mode, the file\\n permissions are preserved.\\n \\\"\\\"\\\"\\n\\n filename = self.temp('test.fits')\\n hdul = [fits.PrimaryHDU(), fits.ImageHDU()]\\n hdul = fits.HDUList(hdul)\\n hdul.writeto(filename)\\n\\n old_mode = os.stat(filename).st_mode\\n\\n hdul = fits.open(filename, mode='update')\\n hdul.insert(1, fits.ImageHDU())\\n hdul.flush()\\n hdul.close()\\n\\n assert old_mode == os.stat(filename).st_mode\\n\\n def test_fileobj_mode_guessing(self):\\n \\\"\\\"\\\"Tests whether a file opened without a specified io.fits mode\\n ('readonly', etc.) is opened in a mode appropriate for the given file\\n object.\\n \\\"\\\"\\\"\\n\\n self.copy_file('test0.fits')\\n\\n # Opening in text mode should outright fail\\n for mode in ('r', 'w', 'a'):\\n with open(self.temp('test0.fits'), mode) as f:\\n pytest.raises(ValueError, fits.HDUList.fromfile, f)\\n\\n # Need to re-copy the file since opening it in 'w' mode blew it away\\n self.copy_file('test0.fits')\\n\\n with open(self.temp('test0.fits'), 'rb') as f:\\n with fits.HDUList.fromfile(f) as h:\\n assert h.fileinfo(0)['filemode'] == 'readonly'\\n\\n for mode in ('wb', 'ab'):\\n with open(self.temp('test0.fits'), mode) as f:\\n with fits.HDUList.fromfile(f) as h:\\n # Basically opening empty files for output streaming\\n assert len(h) == 0\\n\\n # Need to re-copy the file since opening it in 'w' mode blew it away\\n self.copy_file('test0.fits')\\n\\n with open(self.temp('test0.fits'), 'wb+') as f:\\n with fits.HDUList.fromfile(f) as h:\\n # wb+ still causes an existing file to be overwritten so there\\n # are no HDUs\\n assert len(h) == 0\\n\\n # Need to re-copy the file since opening it in 'w' mode blew it away\\n self.copy_file('test0.fits')\\n\\n with open(self.temp('test0.fits'), 'rb+') as f:\\n with fits.HDUList.fromfile(f) as h:\\n assert h.fileinfo(0)['filemode'] == 'update'\\n\\n with open(self.temp('test0.fits'), 'ab+') as f:\\n with fits.HDUList.fromfile(f) as h:\\n assert h.fileinfo(0)['filemode'] == 'append'\\n\\n def test_mmap_unwriteable(self):\\n \\\"\\\"\\\"Regression test for https://github.com/astropy/astropy/issues/968\\n\\n Temporarily patches mmap.mmap to exhibit platform-specific bad\\n behavior.\\n \\\"\\\"\\\"\\n\\n class MockMmap(mmap.mmap):\\n def flush(self):\\n raise OSError('flush is broken on this platform')\\n\\n old_mmap = mmap.mmap\\n mmap.mmap = MockMmap\\n\\n # Force the mmap test to be rerun\\n _File.__dict__['_mmap_available']._cache.clear()\\n\\n try:\\n self.copy_file('test0.fits')\\n with catch_warnings() as w:\\n with fits.open(self.temp('test0.fits'), mode='update',\\n memmap=True) as h:\\n h[1].data[0, 0] = 999\\n\\n assert len(w) == 1\\n assert 'mmap.flush is unavailable' in str(w[0].message)\\n\\n # Double check that writing without mmap still worked\\n with fits.open(self.temp('test0.fits')) as h:\\n assert h[1].data[0, 0] == 999\\n finally:\\n mmap.mmap = old_mmap\\n _File.__dict__['_mmap_available']._cache.clear()\\n\\n @pytest.mark.openfiles_ignore\\n def test_mmap_allocate_error(self):\\n \\\"\\\"\\\"\\n Regression test for https://github.com/astropy/astropy/issues/1380\\n\\n Temporarily patches mmap.mmap to raise an OSError if mode is ACCESS_COPY.\\n \\\"\\\"\\\"\\n\\n mmap_original = mmap.mmap\\n\\n # We patch mmap here to raise an error if access=mmap.ACCESS_COPY, which\\n # emulates an issue that an OSError is raised if the available address\\n # space is less than the size of the file even if memory mapping is used.\\n\\n def mmap_patched(*args, **kwargs):\\n if kwargs.get('access') == mmap.ACCESS_COPY:\\n exc = OSError()\\n exc.errno = errno.ENOMEM\\n raise exc\\n else:\\n return mmap_original(*args, **kwargs)\\n\\n with fits.open(self.data('test0.fits'), memmap=True) as hdulist:\\n with patch.object(mmap, 'mmap', side_effect=mmap_patched) as p:\\n with pytest.warns(AstropyUserWarning, match=r\\\"Could not memory \\\"\\n r\\\"map array with mode='readonly'\\\"):\\n data = hdulist[1].data\\n p.reset_mock()\\n assert not data.flags.writeable\\n\\n def test_mmap_closing(self):\\n \\\"\\\"\\\"\\n Tests that the mmap reference is closed/removed when there aren't any\\n HDU data references left.\\n \\\"\\\"\\\"\\n\\n if not _File._mmap_available:\\n pytest.xfail('not expected to work on platforms without mmap '\\n 'support')\\n\\n with fits.open(self.data('test0.fits'), memmap=True) as hdul:\\n assert hdul._file._mmap is None\\n\\n hdul[1].data\\n assert hdul._file._mmap is not None\\n\\n del hdul[1].data\\n # Should be no more references to data in the file so close the\\n # mmap\\n assert hdul._file._mmap is None\\n\\n hdul[1].data\\n hdul[2].data\\n del hdul[1].data\\n # hdul[2].data is still references so keep the mmap open\\n assert hdul._file._mmap is not None\\n del hdul[2].data\\n assert hdul._file._mmap is None\\n\\n assert hdul._file._mmap is None\\n\\n with fits.open(self.data('test0.fits'), memmap=True) as hdul:\\n hdul[1].data\\n\\n # When the only reference to the data is on the hdu object, and the\\n # hdulist it belongs to has been closed, the mmap should be closed as\\n # well\\n assert hdul._file._mmap is None\\n\\n with fits.open(self.data('test0.fits'), memmap=True) as hdul:\\n data = hdul[1].data\\n # also make a copy\\n data_copy = data.copy()\\n\\n # The HDUList is closed; in fact, get rid of it completely\\n del hdul\\n\\n # The data array should still work though...\\n assert np.all(data == data_copy)\\n\\n def test_uncloseable_file(self):\\n \\\"\\\"\\\"\\n Regression test for https://github.com/astropy/astropy/issues/2356\\n\\n Demonstrates that FITS files can still be read from \\\"file-like\\\" objects\\n that don't have an obvious \\\"open\\\" or \\\"closed\\\" state.\\n \\\"\\\"\\\"\\n\\n class MyFileLike:\\n def __init__(self, foobar):\\n self._foobar = foobar\\n\\n def read(self, n):\\n return self._foobar.read(n)\\n\\n def seek(self, offset, whence=os.SEEK_SET):\\n self._foobar.seek(offset, whence)\\n\\n def tell(self):\\n return self._foobar.tell()\\n\\n with open(self.data('test0.fits'), 'rb') as f:\\n fileobj = MyFileLike(f)\\n\\n with fits.open(fileobj) as hdul1:\\n with fits.open(self.data('test0.fits')) as hdul2:\\n assert hdul1.info(output=False) == hdul2.info(output=False)\\n for hdu1, hdu2 in zip(hdul1, hdul2):\\n assert hdu1.header == hdu2.header\\n if hdu1.data is not None and hdu2.data is not None:\\n assert np.all(hdu1.data == hdu2.data)\\n\\n def test_write_bytesio_discontiguous(self):\\n \\\"\\\"\\\"\\n Regression test related to\\n https://github.com/astropy/astropy/issues/2794#issuecomment-55441539\\n\\n Demonstrates that writing an HDU containing a discontiguous Numpy array\\n should work properly.\\n \\\"\\\"\\\"\\n\\n data = np.arange(100)[::3]\\n hdu = fits.PrimaryHDU(data=data)\\n fileobj = io.BytesIO()\\n hdu.writeto(fileobj)\\n\\n fileobj.seek(0)\\n\\n with fits.open(fileobj) as h:\\n assert np.all(h[0].data == data)\\n\\n def test_write_bytesio(self):\\n \\\"\\\"\\\"\\n Regression test for https://github.com/astropy/astropy/issues/2463\\n\\n Test againt `io.BytesIO`. `io.StringIO` is not supported.\\n \\\"\\\"\\\"\\n\\n self._test_write_string_bytes_io(io.BytesIO())\\n\\n @pytest.mark.skipif('sys.platform.startswith(\\\"win32\\\")')\\n def test_filename_with_colon(self):\\n \\\"\\\"\\\"\\n Test reading and writing a file with a colon in the filename.\\n\\n Regression test for https://github.com/astropy/astropy/issues/3122\\n \\\"\\\"\\\"\\n\\n # Skip on Windows since colons in filenames makes NTFS sad.\\n\\n filename = 'APEXHET.2014-04-01T15:18:01.000.fits'\\n hdu = fits.PrimaryHDU(data=np.arange(10))\\n hdu.writeto(self.temp(filename))\\n\\n with fits.open(self.temp(filename)) as hdul:\\n assert np.all(hdul[0].data == hdu.data)\\n\\n def test_writeto_full_disk(self, monkeypatch):\\n \\\"\\\"\\\"\\n Test that it gives a readable error when trying to write an hdulist\\n to a full disk.\\n \\\"\\\"\\\"\\n\\n def _writeto(self, array):\\n raise OSError(\\\"Fake error raised when writing file.\\\")\\n\\n def get_free_space_in_dir(path):\\n return 0\\n\\n with pytest.raises(OSError) as exc:\\n monkeypatch.setattr(fits.hdu.base._BaseHDU, \\\"_writeto\\\", _writeto)\\n monkeypatch.setattr(data, \\\"get_free_space_in_dir\\\", get_free_space_in_dir)\\n\\n n = np.arange(0, 1000, dtype='int64')\\n hdu = fits.PrimaryHDU(n)\\n hdulist = fits.HDUList(hdu)\\n filename = self.temp('test.fits')\\n\\n with open(filename, mode='wb') as fileobj:\\n hdulist.writeto(fileobj)\\n\\n assert (\\\"Not enough space on disk: requested 8000, available 0. \\\"\\n \\\"Fake error raised when writing file.\\\") == exc.value.args[0]\\n\\n def test_flush_full_disk(self, monkeypatch):\\n \\\"\\\"\\\"\\n Test that it gives a readable error when trying to update an hdulist\\n to a full disk.\\n \\\"\\\"\\\"\\n filename = self.temp('test.fits')\\n hdul = [fits.PrimaryHDU(), fits.ImageHDU()]\\n hdul = fits.HDUList(hdul)\\n hdul[0].data = np.arange(0, 1000, dtype='int64')\\n hdul.writeto(filename)\\n\\n def _writedata(self, fileobj):\\n raise OSError(\\\"Fake error raised when writing file.\\\")\\n\\n def get_free_space_in_dir(path):\\n return 0\\n\\n monkeypatch.setattr(fits.hdu.base._BaseHDU, \\\"_writedata\\\", _writedata)\\n monkeypatch.setattr(data, \\\"get_free_space_in_dir\\\",\\n get_free_space_in_dir)\\n\\n with pytest.raises(OSError) as exc:\\n with fits.open(filename, mode='update') as hdul:\\n hdul[0].data = np.arange(0, 1000, dtype='int64')\\n hdul.insert(1, fits.ImageHDU())\\n hdul.flush()\\n\\n assert (\\\"Not enough space on disk: requested 8000, available 0. \\\"\\n \\\"Fake error raised when writing file.\\\") == exc.value.args[0]\\n\\n def _test_write_string_bytes_io(self, fileobj):\\n \\\"\\\"\\\"\\n Implemented for both test_write_stringio and test_write_bytesio.\\n \\\"\\\"\\\"\\n\\n with fits.open(self.data('test0.fits')) as hdul:\\n hdul.writeto(fileobj)\\n hdul2 = fits.HDUList.fromstring(fileobj.getvalue())\\n assert FITSDiff(hdul, hdul2).identical\\n\\n def _make_gzip_file(self, filename='test0.fits.gz'):\\n gzfile = self.temp(filename)\\n with open(self.data('test0.fits'), 'rb') as f:\\n gz = gzip.open(gzfile, 'wb')\\n gz.write(f.read())\\n gz.close()\\n\\n return gzfile\\n\\n def _make_zip_file(self, mode='copyonwrite', filename='test0.fits.zip'):\\n zfile = zipfile.ZipFile(self.temp(filename), 'w')\\n zfile.write(self.data('test0.fits'))\\n zfile.close()\\n\\n return zfile.filename\\n\\n def _make_bzip2_file(self, filename='test0.fits.bz2'):\\n bzfile = self.temp(filename)\\n with open(self.data('test0.fits'), 'rb') as f:\\n bz = bz2.BZ2File(bzfile, 'w')\\n bz.write(f.read())\\n bz.close()\\n\\n return bzfile\\n\\n def test_simulateonly(self):\\n \\\"\\\"\\\"Write to None simulates writing.\\\"\\\"\\\"\\n\\n with fits.open(self.data('test0.fits')) as hdul:\\n hdul.writeto(None)\\n hdul[0].writeto(None)\\n hdul[0].header.tofile(None)\\n\\n\\nclass TestStreamingFunctions(FitsTestCase):\\n \\\"\\\"\\\"Test functionality of the StreamingHDU class.\\\"\\\"\\\"\\n\\n def test_streaming_hdu(self):\\n shdu = self._make_streaming_hdu(self.temp('new.fits'))\\n assert isinstance(shdu.size, int)\\n assert shdu.size == 100\\n shdu.close()\\n\\n @raises(ValueError)\\n def test_streaming_hdu_file_wrong_mode(self):\\n \\\"\\\"\\\"\\n Test that streaming an HDU to a file opened in the wrong mode fails as\\n expected.\\n \\\"\\\"\\\"\\n\\n with open(self.temp('new.fits'), 'wb') as f:\\n header = fits.Header()\\n fits.StreamingHDU(f, header)\\n\\n def test_streaming_hdu_write_file(self):\\n \\\"\\\"\\\"Test streaming an HDU to an open file object.\\\"\\\"\\\"\\n\\n arr = np.zeros((5, 5), dtype=np.int32)\\n with open(self.temp('new.fits'), 'ab+') as f:\\n shdu = self._make_streaming_hdu(f)\\n shdu.write(arr)\\n assert shdu.writecomplete\\n assert shdu.size == 100\\n with fits.open(self.temp('new.fits')) as hdul:\\n assert len(hdul) == 1\\n assert (hdul[0].data == arr).all()\\n\\n def test_streaming_hdu_write_file_like(self):\\n \\\"\\\"\\\"Test streaming an HDU to an open file-like object.\\\"\\\"\\\"\\n\\n arr = np.zeros((5, 5), dtype=np.int32)\\n # The file-like object underlying a StreamingHDU must be in binary mode\\n sf = io.BytesIO()\\n shdu = self._make_streaming_hdu(sf)\\n shdu.write(arr)\\n assert shdu.writecomplete\\n assert shdu.size == 100\\n\\n sf.seek(0)\\n hdul = fits.open(sf)\\n assert len(hdul) == 1\\n assert (hdul[0].data == arr).all()\\n\\n def test_streaming_hdu_append_extension(self):\\n arr = np.zeros((5, 5), dtype=np.int32)\\n with open(self.temp('new.fits'), 'ab+') as f:\\n shdu = self._make_streaming_hdu(f)\\n shdu.write(arr)\\n # Doing this again should update the file with an extension\\n with open(self.temp('new.fits'), 'ab+') as f:\\n shdu = self._make_streaming_hdu(f)\\n shdu.write(arr)\\n\\n def test_fix_invalid_extname(self, capsys):\\n phdu = fits.PrimaryHDU()\\n ihdu = fits.ImageHDU()\\n ihdu.header['EXTNAME'] = 12345678\\n hdul = fits.HDUList([phdu, ihdu])\\n filename = self.temp('temp.fits')\\n\\n pytest.raises(fits.VerifyError, hdul.writeto, filename,\\n output_verify='exception')\\n with pytest.warns(fits.verify.VerifyWarning,\\n match=r'Verification reported errors'):\\n hdul.writeto(filename, output_verify='fix')\\n with fits.open(filename):\\n assert hdul[1].name == '12345678'\\n assert hdul[1].header['EXTNAME'] == '12345678'\\n\\n hdul.close()\\n\\n def _make_streaming_hdu(self, fileobj):\\n hd = fits.Header()\\n hd['SIMPLE'] = (True, 'conforms to FITS standard')\\n hd['BITPIX'] = (32, 'array data type')\\n hd['NAXIS'] = (2, 'number of array dimensions')\\n hd['NAXIS1'] = 5\\n hd['NAXIS2'] = 5\\n hd['EXTEND'] = True\\n return fits.StreamingHDU(fileobj, hd)\\n\\n def test_blank_ignore(self):\\n\\n with fits.open(self.data('blank.fits'), ignore_blank=True) as f:\\n assert f[0].data.flat[0] == 2\\n\\n def test_error_if_memmap_impossible(self):\\n pth = self.data('blank.fits')\\n with fits.open(pth, memmap=True) as hdul:\\n with pytest.raises(ValueError):\\n hdul[0].data\\n\\n # However, it should not fail if do_not_scale_image_data was used:\\n # See https://github.com/astropy/astropy/issues/3766\\n with fits.open(pth, memmap=True, do_not_scale_image_data=True) as hdul:\\n hdul[0].data # Just make sure it doesn't crash\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.logical_not","numpy.all","numpy.isscalar","numpy.array","numpy.zeros"],["numpy.all","numpy.arange","numpy.zeros"]],"string":"[\n [\n \"numpy.logical_not\",\n \"numpy.all\",\n \"numpy.isscalar\",\n \"numpy.array\",\n \"numpy.zeros\"\n ],\n [\n \"numpy.all\",\n \"numpy.arange\",\n \"numpy.zeros\"\n ]\n]","isConversational":false}}},{"rowIdx":1296,"cells":{"repo_name":{"kind":"string","value":"LauraOlivera/gammapy"},"hexsha":{"kind":"list like","value":["8aadf0ec524bcf51d0ac5655a04507d5d449e7ed"],"string":"[\n \"8aadf0ec524bcf51d0ac5655a04507d5d449e7ed\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["gammapy/estimators/tests/test_flux_point.py"],"string":"[\n \"gammapy/estimators/tests/test_flux_point.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport pytest\nimport numpy as np\nfrom numpy.testing import assert_allclose\nimport astropy.units as u\nfrom astropy.table import Table\nfrom gammapy.catalog.fermi import SourceCatalog3FGL\nfrom gammapy.estimators import FluxPoints\nfrom gammapy.modeling.models import SpectralModel\nfrom gammapy.utils.scripts import make_path\nfrom gammapy.utils.testing import (\n assert_quantity_allclose,\n mpl_plot_check,\n requires_data,\n requires_dependency,\n)\n\nFLUX_POINTS_FILES = [\n \"diff_flux_points.ecsv\",\n \"diff_flux_points.fits\",\n \"flux_points.ecsv\",\n \"flux_points.fits\",\n]\n\n\nclass LWTestModel(SpectralModel):\n @staticmethod\n def evaluate(x):\n return 1e4 * np.exp(-6 * x)\n\n def integral(self, xmin, xmax, **kwargs):\n return -1.0 / 6 * 1e4 * (np.exp(-6 * xmax) - np.exp(-6 * xmin))\n\n def inverse(self, y):\n return -1.0 / 6 * np.log(y * 1e-4)\n\n\nclass XSqrTestModel(SpectralModel):\n @staticmethod\n def evaluate(x):\n return x ** 2\n\n def integral(self, xmin, xmax, **kwargs):\n return 1.0 / 3 * (xmax ** 3 - xmin ** 2)\n\n def inverse(self, y):\n return np.sqrt(y)\n\n\nclass ExpTestModel(SpectralModel):\n @staticmethod\n def evaluate(x):\n return np.exp(x * u.Unit(\"1 / TeV\"))\n\n def integral(self, xmin, xmax, **kwargs):\n return np.exp(xmax * u.Unit(\"1 / TeV\")) - np.exp(xmin * u.Unit(\"1 / TeV\"))\n\n def inverse(self, y):\n return np.log(y * u.TeV) * u.TeV\n\n\ndef test_energy_ref_lafferty():\n \"\"\"\n Tests Lafferty & Wyatt x-point method.\n\n Using input function g(x) = 10^4 exp(-6x) against\n check values from paper Lafferty & Wyatt. Nucl. Instr. and Meth. in Phys.\n Res. A 355 (1995) 541-547, p. 542 Table 1\n \"\"\"\n # These are the results from the paper\n desired = np.array([0.048, 0.190, 0.428, 0.762])\n\n model = LWTestModel()\n energy_min = np.array([0.0, 0.1, 0.3, 0.6])\n energy_max = np.array([0.1, 0.3, 0.6, 1.0])\n actual = FluxPoints._energy_ref_lafferty(model, energy_min, energy_max)\n assert_allclose(actual, desired, atol=1e-3)\n\n\n@pytest.mark.xfail\ndef test_dnde_from_flux():\n \"\"\"Tests y-value normalization adjustment method.\n \"\"\"\n table = Table()\n table[\"e_min\"] = np.array([10, 20, 30, 40])\n table[\"e_max\"] = np.array([20, 30, 40, 50])\n table[\"flux\"] = np.array([42, 52, 62, 72]) # 'True' integral flux in this test bin\n\n # Get values\n model = XSqrTestModel()\n table[\"e_ref\"] = FluxPoints._energy_ref_lafferty(model, table[\"e_min\"], table[\"e_max\"])\n dnde = FluxPoints.from_table(table, reference_model=model)\n\n # Set up test case comparison\n dnde_model = model(table[\"e_ref\"])\n\n # Test comparison result\n desired = model.integral(table[\"e_min\"], table[\"e_max\"])\n # Test output result\n actual = table[\"flux\"] * (dnde_model / dnde)\n # Compare\n assert_allclose(actual, desired, rtol=1e-6)\n\n\n@pytest.mark.xfail\n@pytest.mark.parametrize(\"method\", [\"table\", \"lafferty\", \"log_center\"])\ndef test_compute_flux_points_dnde_exp(method):\n \"\"\"\n Tests against analytical result or result from a powerlaw.\n \"\"\"\n model = ExpTestModel()\n\n energy_min = [1.0, 10.0] * u.TeV\n energy_max = [10.0, 100.0] * u.TeV\n\n table = Table()\n table.meta[\"SED_TYPE\"] = \"flux\"\n table[\"e_min\"] = energy_min\n table[\"e_max\"] = energy_max\n\n flux = model.integral(energy_min, energy_max)\n table[\"flux\"] = flux\n\n if method == \"log_center\":\n energy_ref = np.sqrt(energy_min * energy_max)\n elif method == \"table\":\n energy_ref = [2.0, 20.0] * u.TeV\n elif method == \"lafferty\":\n energy_ref = FluxPoints._energy_ref_lafferty(model, energy_min, energy_max)\n\n table[\"e_ref\"] = energy_ref\n\n result = FluxPoints.from_table(table, reference_model=model)\n\n # Test energy\n actual = result.energy_ref\n assert_quantity_allclose(actual, energy_ref, rtol=1e-8)\n\n # Test flux\n actual = result.dnde\n desired = model(energy_ref)\n assert_quantity_allclose(actual, desired, rtol=1e-8)\n\n\n@requires_data()\ndef test_fermi_to_dnde():\n from gammapy.catalog import SourceCatalog4FGL\n\n catalog_4fgl = SourceCatalog4FGL(\"$GAMMAPY_DATA/catalogs/fermi/gll_psc_v20.fit.gz\")\n src = catalog_4fgl[\"FGES J1553.8-5325\"]\n fp = src.flux_points\n\n assert_allclose(\n fp.dnde.quantity[1, 0, 0],\n 4.567393e-10 * u.Unit(\"cm-2 s-1 MeV-1\"),\n rtol=1e-5,\n )\n\n\n@pytest.fixture(params=FLUX_POINTS_FILES, scope=\"session\")\ndef flux_points(request):\n path = \"$GAMMAPY_DATA/tests/spectrum/flux_points/\" + request.param\n return FluxPoints.read(path)\n\n\n@pytest.fixture(scope=\"session\")\ndef flux_points_likelihood():\n path = \"$GAMMAPY_DATA/tests/spectrum/flux_points/binlike.fits\"\n return FluxPoints.read(path)\n\n\n@requires_data()\nclass TestFluxPoints:\n def test_info(self, flux_points):\n info = str(flux_points)\n assert \"geom\" in info\n assert \"axes\" in info\n assert \"ref. model\" in info\n assert \"quantities\" in info\n\n def test_energy_ref(self, flux_points):\n actual = flux_points.energy_ref\n desired = np.sqrt(flux_points.energy_min * flux_points.energy_max)\n assert_quantity_allclose(actual, desired)\n\n def test_energy_min(self, flux_points):\n actual = flux_points.energy_min\n desired = 299530.97 * u.MeV\n assert_quantity_allclose(actual.sum(), desired)\n\n def test_energy_max(self, flux_points):\n actual = flux_points.energy_max\n desired = 399430.975 * u.MeV\n assert_quantity_allclose(actual.sum(), desired)\n\n def test_write_fits(self, tmp_path, flux_points):\n flux_points.write(tmp_path / \"tmp.fits\", sed_type=flux_points.sed_type_init)\n actual = FluxPoints.read(tmp_path / \"tmp.fits\")\n assert str(flux_points) == str(actual)\n\n def test_write_ecsv(self, tmp_path, flux_points):\n flux_points.write(tmp_path / \"flux_points.ecsv\", sed_type=flux_points.sed_type_init)\n actual = FluxPoints.read(tmp_path / \"flux_points.ecsv\")\n assert str(flux_points) == str(actual)\n\n def test_quantity_access(self, flux_points_likelihood):\n assert flux_points_likelihood.sqrt_ts\n assert flux_points_likelihood.ts\n assert flux_points_likelihood.stat\n assert_allclose(flux_points_likelihood.n_sigma_ul, 2)\n assert flux_points_likelihood.sed_type_init == \"likelihood\"\n\n @requires_dependency(\"matplotlib\")\n def test_plot(self, flux_points):\n with mpl_plot_check():\n flux_points.plot()\n\n @requires_dependency(\"matplotlib\")\n def test_plot_likelihood(self, flux_points_likelihood):\n with mpl_plot_check():\n flux_points_likelihood.plot_ts_profiles()\n\n @requires_dependency(\"matplotlib\")\n def test_plot_likelihood_error(self, flux_points_likelihood):\n del flux_points_likelihood._data[\"stat_scan\"]\n with pytest.raises(AttributeError):\n flux_points_likelihood.plot_ts_profiles()\n\n\n@requires_data()\ndef test_compute_flux_points_dnde_fermi():\n \"\"\"\n Test compute_flux_points_dnde on fermi source.\n \"\"\"\n fermi_3fgl = SourceCatalog3FGL()\n source = fermi_3fgl[\"3FGL J0835.3-4510\"]\n flux_points = source.flux_points\n table = source.flux_points_table\n\n for column in [\"e2dnde\", \"e2dnde_errn\", \"e2dnde_errp\", \"e2dnde_ul\"]:\n actual = table[column].quantity\n desired = getattr(flux_points, column).quantity.squeeze()\n assert_quantity_allclose(actual[:-1], desired[:-1], rtol=0.05)\n\n@requires_data()\n@requires_dependency(\"matplotlib\")\ndef test_plot_fp_no_ul():\n path = make_path(\"$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits\")\n table = Table.read(path)\n table.remove_column('dnde_ul')\n fp = FluxPoints.from_table(table, sed_type='dnde')\n\n with mpl_plot_check():\n fp.plot()\n\n"],"string":"[\n \"# Licensed under a 3-clause BSD style license - see LICENSE.rst\\nimport pytest\\nimport numpy as np\\nfrom numpy.testing import assert_allclose\\nimport astropy.units as u\\nfrom astropy.table import Table\\nfrom gammapy.catalog.fermi import SourceCatalog3FGL\\nfrom gammapy.estimators import FluxPoints\\nfrom gammapy.modeling.models import SpectralModel\\nfrom gammapy.utils.scripts import make_path\\nfrom gammapy.utils.testing import (\\n assert_quantity_allclose,\\n mpl_plot_check,\\n requires_data,\\n requires_dependency,\\n)\\n\\nFLUX_POINTS_FILES = [\\n \\\"diff_flux_points.ecsv\\\",\\n \\\"diff_flux_points.fits\\\",\\n \\\"flux_points.ecsv\\\",\\n \\\"flux_points.fits\\\",\\n]\\n\\n\\nclass LWTestModel(SpectralModel):\\n @staticmethod\\n def evaluate(x):\\n return 1e4 * np.exp(-6 * x)\\n\\n def integral(self, xmin, xmax, **kwargs):\\n return -1.0 / 6 * 1e4 * (np.exp(-6 * xmax) - np.exp(-6 * xmin))\\n\\n def inverse(self, y):\\n return -1.0 / 6 * np.log(y * 1e-4)\\n\\n\\nclass XSqrTestModel(SpectralModel):\\n @staticmethod\\n def evaluate(x):\\n return x ** 2\\n\\n def integral(self, xmin, xmax, **kwargs):\\n return 1.0 / 3 * (xmax ** 3 - xmin ** 2)\\n\\n def inverse(self, y):\\n return np.sqrt(y)\\n\\n\\nclass ExpTestModel(SpectralModel):\\n @staticmethod\\n def evaluate(x):\\n return np.exp(x * u.Unit(\\\"1 / TeV\\\"))\\n\\n def integral(self, xmin, xmax, **kwargs):\\n return np.exp(xmax * u.Unit(\\\"1 / TeV\\\")) - np.exp(xmin * u.Unit(\\\"1 / TeV\\\"))\\n\\n def inverse(self, y):\\n return np.log(y * u.TeV) * u.TeV\\n\\n\\ndef test_energy_ref_lafferty():\\n \\\"\\\"\\\"\\n Tests Lafferty & Wyatt x-point method.\\n\\n Using input function g(x) = 10^4 exp(-6x) against\\n check values from paper Lafferty & Wyatt. Nucl. Instr. and Meth. in Phys.\\n Res. A 355 (1995) 541-547, p. 542 Table 1\\n \\\"\\\"\\\"\\n # These are the results from the paper\\n desired = np.array([0.048, 0.190, 0.428, 0.762])\\n\\n model = LWTestModel()\\n energy_min = np.array([0.0, 0.1, 0.3, 0.6])\\n energy_max = np.array([0.1, 0.3, 0.6, 1.0])\\n actual = FluxPoints._energy_ref_lafferty(model, energy_min, energy_max)\\n assert_allclose(actual, desired, atol=1e-3)\\n\\n\\n@pytest.mark.xfail\\ndef test_dnde_from_flux():\\n \\\"\\\"\\\"Tests y-value normalization adjustment method.\\n \\\"\\\"\\\"\\n table = Table()\\n table[\\\"e_min\\\"] = np.array([10, 20, 30, 40])\\n table[\\\"e_max\\\"] = np.array([20, 30, 40, 50])\\n table[\\\"flux\\\"] = np.array([42, 52, 62, 72]) # 'True' integral flux in this test bin\\n\\n # Get values\\n model = XSqrTestModel()\\n table[\\\"e_ref\\\"] = FluxPoints._energy_ref_lafferty(model, table[\\\"e_min\\\"], table[\\\"e_max\\\"])\\n dnde = FluxPoints.from_table(table, reference_model=model)\\n\\n # Set up test case comparison\\n dnde_model = model(table[\\\"e_ref\\\"])\\n\\n # Test comparison result\\n desired = model.integral(table[\\\"e_min\\\"], table[\\\"e_max\\\"])\\n # Test output result\\n actual = table[\\\"flux\\\"] * (dnde_model / dnde)\\n # Compare\\n assert_allclose(actual, desired, rtol=1e-6)\\n\\n\\n@pytest.mark.xfail\\n@pytest.mark.parametrize(\\\"method\\\", [\\\"table\\\", \\\"lafferty\\\", \\\"log_center\\\"])\\ndef test_compute_flux_points_dnde_exp(method):\\n \\\"\\\"\\\"\\n Tests against analytical result or result from a powerlaw.\\n \\\"\\\"\\\"\\n model = ExpTestModel()\\n\\n energy_min = [1.0, 10.0] * u.TeV\\n energy_max = [10.0, 100.0] * u.TeV\\n\\n table = Table()\\n table.meta[\\\"SED_TYPE\\\"] = \\\"flux\\\"\\n table[\\\"e_min\\\"] = energy_min\\n table[\\\"e_max\\\"] = energy_max\\n\\n flux = model.integral(energy_min, energy_max)\\n table[\\\"flux\\\"] = flux\\n\\n if method == \\\"log_center\\\":\\n energy_ref = np.sqrt(energy_min * energy_max)\\n elif method == \\\"table\\\":\\n energy_ref = [2.0, 20.0] * u.TeV\\n elif method == \\\"lafferty\\\":\\n energy_ref = FluxPoints._energy_ref_lafferty(model, energy_min, energy_max)\\n\\n table[\\\"e_ref\\\"] = energy_ref\\n\\n result = FluxPoints.from_table(table, reference_model=model)\\n\\n # Test energy\\n actual = result.energy_ref\\n assert_quantity_allclose(actual, energy_ref, rtol=1e-8)\\n\\n # Test flux\\n actual = result.dnde\\n desired = model(energy_ref)\\n assert_quantity_allclose(actual, desired, rtol=1e-8)\\n\\n\\n@requires_data()\\ndef test_fermi_to_dnde():\\n from gammapy.catalog import SourceCatalog4FGL\\n\\n catalog_4fgl = SourceCatalog4FGL(\\\"$GAMMAPY_DATA/catalogs/fermi/gll_psc_v20.fit.gz\\\")\\n src = catalog_4fgl[\\\"FGES J1553.8-5325\\\"]\\n fp = src.flux_points\\n\\n assert_allclose(\\n fp.dnde.quantity[1, 0, 0],\\n 4.567393e-10 * u.Unit(\\\"cm-2 s-1 MeV-1\\\"),\\n rtol=1e-5,\\n )\\n\\n\\n@pytest.fixture(params=FLUX_POINTS_FILES, scope=\\\"session\\\")\\ndef flux_points(request):\\n path = \\\"$GAMMAPY_DATA/tests/spectrum/flux_points/\\\" + request.param\\n return FluxPoints.read(path)\\n\\n\\n@pytest.fixture(scope=\\\"session\\\")\\ndef flux_points_likelihood():\\n path = \\\"$GAMMAPY_DATA/tests/spectrum/flux_points/binlike.fits\\\"\\n return FluxPoints.read(path)\\n\\n\\n@requires_data()\\nclass TestFluxPoints:\\n def test_info(self, flux_points):\\n info = str(flux_points)\\n assert \\\"geom\\\" in info\\n assert \\\"axes\\\" in info\\n assert \\\"ref. model\\\" in info\\n assert \\\"quantities\\\" in info\\n\\n def test_energy_ref(self, flux_points):\\n actual = flux_points.energy_ref\\n desired = np.sqrt(flux_points.energy_min * flux_points.energy_max)\\n assert_quantity_allclose(actual, desired)\\n\\n def test_energy_min(self, flux_points):\\n actual = flux_points.energy_min\\n desired = 299530.97 * u.MeV\\n assert_quantity_allclose(actual.sum(), desired)\\n\\n def test_energy_max(self, flux_points):\\n actual = flux_points.energy_max\\n desired = 399430.975 * u.MeV\\n assert_quantity_allclose(actual.sum(), desired)\\n\\n def test_write_fits(self, tmp_path, flux_points):\\n flux_points.write(tmp_path / \\\"tmp.fits\\\", sed_type=flux_points.sed_type_init)\\n actual = FluxPoints.read(tmp_path / \\\"tmp.fits\\\")\\n assert str(flux_points) == str(actual)\\n\\n def test_write_ecsv(self, tmp_path, flux_points):\\n flux_points.write(tmp_path / \\\"flux_points.ecsv\\\", sed_type=flux_points.sed_type_init)\\n actual = FluxPoints.read(tmp_path / \\\"flux_points.ecsv\\\")\\n assert str(flux_points) == str(actual)\\n\\n def test_quantity_access(self, flux_points_likelihood):\\n assert flux_points_likelihood.sqrt_ts\\n assert flux_points_likelihood.ts\\n assert flux_points_likelihood.stat\\n assert_allclose(flux_points_likelihood.n_sigma_ul, 2)\\n assert flux_points_likelihood.sed_type_init == \\\"likelihood\\\"\\n\\n @requires_dependency(\\\"matplotlib\\\")\\n def test_plot(self, flux_points):\\n with mpl_plot_check():\\n flux_points.plot()\\n\\n @requires_dependency(\\\"matplotlib\\\")\\n def test_plot_likelihood(self, flux_points_likelihood):\\n with mpl_plot_check():\\n flux_points_likelihood.plot_ts_profiles()\\n\\n @requires_dependency(\\\"matplotlib\\\")\\n def test_plot_likelihood_error(self, flux_points_likelihood):\\n del flux_points_likelihood._data[\\\"stat_scan\\\"]\\n with pytest.raises(AttributeError):\\n flux_points_likelihood.plot_ts_profiles()\\n\\n\\n@requires_data()\\ndef test_compute_flux_points_dnde_fermi():\\n \\\"\\\"\\\"\\n Test compute_flux_points_dnde on fermi source.\\n \\\"\\\"\\\"\\n fermi_3fgl = SourceCatalog3FGL()\\n source = fermi_3fgl[\\\"3FGL J0835.3-4510\\\"]\\n flux_points = source.flux_points\\n table = source.flux_points_table\\n\\n for column in [\\\"e2dnde\\\", \\\"e2dnde_errn\\\", \\\"e2dnde_errp\\\", \\\"e2dnde_ul\\\"]:\\n actual = table[column].quantity\\n desired = getattr(flux_points, column).quantity.squeeze()\\n assert_quantity_allclose(actual[:-1], desired[:-1], rtol=0.05)\\n\\n@requires_data()\\n@requires_dependency(\\\"matplotlib\\\")\\ndef test_plot_fp_no_ul():\\n path = make_path(\\\"$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits\\\")\\n table = Table.read(path)\\n table.remove_column('dnde_ul')\\n fp = FluxPoints.from_table(table, sed_type='dnde')\\n\\n with mpl_plot_check():\\n fp.plot()\\n\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.log","numpy.sqrt","numpy.testing.assert_allclose","numpy.array","numpy.exp"]],"string":"[\n [\n \"numpy.log\",\n \"numpy.sqrt\",\n \"numpy.testing.assert_allclose\",\n \"numpy.array\",\n \"numpy.exp\"\n ]\n]","isConversational":false}}},{"rowIdx":1297,"cells":{"repo_name":{"kind":"string","value":"MaxxWilson/ASE389Project"},"hexsha":{"kind":"list like","value":["13c3c72887e27fbed2eef63c1e27b4a185036a39"],"string":"[\n \"13c3c72887e27fbed2eef63c1e27b4a185036a39\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["pnc/draco3_lb_pnc/draco3_lb_controller.py"],"string":"[\n \"pnc/draco3_lb_pnc/draco3_lb_controller.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["import numpy as np\n\nfrom util import util\nfrom config.draco3_lb_config import PnCConfig, WBCConfig\nfrom pnc.wbc.ihwbc.ihwbc import IHWBC\nfrom pnc.wbc.ihwbc.joint_integrator import JointIntegrator\n\n\nclass Draco3LBController(object):\n def __init__(self, tci_container, robot):\n self._tci_container = tci_container\n self._robot = robot\n\n # Initialize WBC\n l_jp_idx, l_jd_idx, r_jp_idx, r_jd_idx = self._robot.get_q_dot_idx(\n ['l_knee_fe_jp', 'l_knee_fe_jd', 'r_knee_fe_jp', 'r_knee_fe_jd'])\n act_list = [False] * robot.n_floating + [True] * robot.n_a\n act_list[l_jd_idx] = False\n act_list[r_jd_idx] = False\n\n n_q_dot = len(act_list)\n n_active = np.count_nonzero(np.array(act_list))\n n_passive = n_q_dot - n_active - 6\n\n self._sa = np.zeros((n_active, n_q_dot))\n self._sv = np.zeros((n_passive, n_q_dot))\n j, k = 0, 0\n for i in range(n_q_dot):\n if i >= 6:\n if act_list[i]:\n self._sa[j, i] = 1.\n j += 1\n else:\n self._sv[k, i] = 1.\n k += 1\n self._sf = np.zeros((6, n_q_dot))\n self._sf[0:6, 0:6] = np.eye(6)\n\n self._ihwbc = IHWBC(self._sf, self._sa, self._sv, PnCConfig.SAVE_DATA)\n if WBCConfig.B_TRQ_LIMIT:\n self._ihwbc.trq_limit = np.dot(self._sa[:, 6:],\n self._robot.joint_trq_limit)\n self._ihwbc.lambda_q_ddot = WBCConfig.LAMBDA_Q_DDOT\n self._ihwbc.lambda_rf = WBCConfig.LAMBDA_RF\n\n # Initialize Joint Integrator\n self._joint_integrator = JointIntegrator(robot.n_a,\n PnCConfig.CONTROLLER_DT)\n self._joint_integrator.pos_cutoff_freq = WBCConfig.POS_CUTOFF_FREQ\n self._joint_integrator.vel_cutoff_freq = WBCConfig.VEL_CUTOFF_FREQ\n self._joint_integrator.max_pos_err = WBCConfig.MAX_POS_ERR\n self._joint_integrator.joint_pos_limit = self._robot.joint_pos_limit\n self._joint_integrator.joint_vel_limit = self._robot.joint_vel_limit\n\n self._b_first_visit = True\n\n def get_command(self):\n if self._b_first_visit:\n self.first_visit()\n\n # Dynamics properties\n mass_matrix = self._robot.get_mass_matrix()\n mass_matrix_inv = np.linalg.inv(mass_matrix)\n coriolis = self._robot.get_coriolis()\n gravity = self._robot.get_gravity()\n self._ihwbc.update_setting(mass_matrix, mass_matrix_inv, coriolis,\n gravity)\n # Task, Contact, and Internal Constraint Setup\n w_hierarchy_list = []\n for task in self._tci_container.task_list:\n task.update_jacobian()\n task.update_cmd()\n w_hierarchy_list.append(task.w_hierarchy)\n self._ihwbc.w_hierarchy = np.array(w_hierarchy_list)\n for contact in self._tci_container.contact_list:\n contact.update_contact()\n for internal_constraint in self._tci_container.internal_constraint_list:\n internal_constraint.update_internal_constraint()\n # WBC commands\n joint_trq_cmd, joint_acc_cmd, rf_cmd = self._ihwbc.solve(\n self._tci_container.task_list, self._tci_container.contact_list,\n self._tci_container.internal_constraint_list)\n joint_trq_cmd = np.dot(self._sa[:, 6:].transpose(), joint_trq_cmd)\n joint_acc_cmd = np.dot(self._sa[:, 6:].transpose(), joint_acc_cmd)\n # Double integration\n joint_vel_cmd, joint_pos_cmd = self._joint_integrator.integrate(\n joint_acc_cmd, self._robot.joint_velocities,\n self._robot.joint_positions)\n\n command = self._robot.create_cmd_ordered_dict(joint_pos_cmd,\n joint_vel_cmd,\n joint_trq_cmd)\n return command\n\n def first_visit(self):\n joint_pos_ini = self._robot.joint_positions\n self._joint_integrator.initialize_states(np.zeros(self._robot.n_a),\n joint_pos_ini)\n\n self._b_first_visit = False\n"],"string":"[\n \"import numpy as np\\n\\nfrom util import util\\nfrom config.draco3_lb_config import PnCConfig, WBCConfig\\nfrom pnc.wbc.ihwbc.ihwbc import IHWBC\\nfrom pnc.wbc.ihwbc.joint_integrator import JointIntegrator\\n\\n\\nclass Draco3LBController(object):\\n def __init__(self, tci_container, robot):\\n self._tci_container = tci_container\\n self._robot = robot\\n\\n # Initialize WBC\\n l_jp_idx, l_jd_idx, r_jp_idx, r_jd_idx = self._robot.get_q_dot_idx(\\n ['l_knee_fe_jp', 'l_knee_fe_jd', 'r_knee_fe_jp', 'r_knee_fe_jd'])\\n act_list = [False] * robot.n_floating + [True] * robot.n_a\\n act_list[l_jd_idx] = False\\n act_list[r_jd_idx] = False\\n\\n n_q_dot = len(act_list)\\n n_active = np.count_nonzero(np.array(act_list))\\n n_passive = n_q_dot - n_active - 6\\n\\n self._sa = np.zeros((n_active, n_q_dot))\\n self._sv = np.zeros((n_passive, n_q_dot))\\n j, k = 0, 0\\n for i in range(n_q_dot):\\n if i >= 6:\\n if act_list[i]:\\n self._sa[j, i] = 1.\\n j += 1\\n else:\\n self._sv[k, i] = 1.\\n k += 1\\n self._sf = np.zeros((6, n_q_dot))\\n self._sf[0:6, 0:6] = np.eye(6)\\n\\n self._ihwbc = IHWBC(self._sf, self._sa, self._sv, PnCConfig.SAVE_DATA)\\n if WBCConfig.B_TRQ_LIMIT:\\n self._ihwbc.trq_limit = np.dot(self._sa[:, 6:],\\n self._robot.joint_trq_limit)\\n self._ihwbc.lambda_q_ddot = WBCConfig.LAMBDA_Q_DDOT\\n self._ihwbc.lambda_rf = WBCConfig.LAMBDA_RF\\n\\n # Initialize Joint Integrator\\n self._joint_integrator = JointIntegrator(robot.n_a,\\n PnCConfig.CONTROLLER_DT)\\n self._joint_integrator.pos_cutoff_freq = WBCConfig.POS_CUTOFF_FREQ\\n self._joint_integrator.vel_cutoff_freq = WBCConfig.VEL_CUTOFF_FREQ\\n self._joint_integrator.max_pos_err = WBCConfig.MAX_POS_ERR\\n self._joint_integrator.joint_pos_limit = self._robot.joint_pos_limit\\n self._joint_integrator.joint_vel_limit = self._robot.joint_vel_limit\\n\\n self._b_first_visit = True\\n\\n def get_command(self):\\n if self._b_first_visit:\\n self.first_visit()\\n\\n # Dynamics properties\\n mass_matrix = self._robot.get_mass_matrix()\\n mass_matrix_inv = np.linalg.inv(mass_matrix)\\n coriolis = self._robot.get_coriolis()\\n gravity = self._robot.get_gravity()\\n self._ihwbc.update_setting(mass_matrix, mass_matrix_inv, coriolis,\\n gravity)\\n # Task, Contact, and Internal Constraint Setup\\n w_hierarchy_list = []\\n for task in self._tci_container.task_list:\\n task.update_jacobian()\\n task.update_cmd()\\n w_hierarchy_list.append(task.w_hierarchy)\\n self._ihwbc.w_hierarchy = np.array(w_hierarchy_list)\\n for contact in self._tci_container.contact_list:\\n contact.update_contact()\\n for internal_constraint in self._tci_container.internal_constraint_list:\\n internal_constraint.update_internal_constraint()\\n # WBC commands\\n joint_trq_cmd, joint_acc_cmd, rf_cmd = self._ihwbc.solve(\\n self._tci_container.task_list, self._tci_container.contact_list,\\n self._tci_container.internal_constraint_list)\\n joint_trq_cmd = np.dot(self._sa[:, 6:].transpose(), joint_trq_cmd)\\n joint_acc_cmd = np.dot(self._sa[:, 6:].transpose(), joint_acc_cmd)\\n # Double integration\\n joint_vel_cmd, joint_pos_cmd = self._joint_integrator.integrate(\\n joint_acc_cmd, self._robot.joint_velocities,\\n self._robot.joint_positions)\\n\\n command = self._robot.create_cmd_ordered_dict(joint_pos_cmd,\\n joint_vel_cmd,\\n joint_trq_cmd)\\n return command\\n\\n def first_visit(self):\\n joint_pos_ini = self._robot.joint_positions\\n self._joint_integrator.initialize_states(np.zeros(self._robot.n_a),\\n joint_pos_ini)\\n\\n self._b_first_visit = False\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["numpy.dot","numpy.linalg.inv","numpy.eye","numpy.array","numpy.zeros"]],"string":"[\n [\n \"numpy.dot\",\n \"numpy.linalg.inv\",\n \"numpy.eye\",\n \"numpy.array\",\n \"numpy.zeros\"\n ]\n]","isConversational":false}}},{"rowIdx":1298,"cells":{"repo_name":{"kind":"string","value":"Barry-lab/Publication_TanniDeCothiBarry2021"},"hexsha":{"kind":"list like","value":["425bc0bd9a74b837d912820e9ea1539a111fcb1f","425bc0bd9a74b837d912820e9ea1539a111fcb1f","425bc0bd9a74b837d912820e9ea1539a111fcb1f"],"string":"[\n \"425bc0bd9a74b837d912820e9ea1539a111fcb1f\",\n \"425bc0bd9a74b837d912820e9ea1539a111fcb1f\",\n \"425bc0bd9a74b837d912820e9ea1539a111fcb1f\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["visual_change_analysis/main.py","electrophysiology_analysis/barrylab_ephys_analysis/spatial/fields.py","electrophysiology_analysis/barrylab_ephys_analysis/lfp/oscillations.py"],"string":"[\n \"visual_change_analysis/main.py\",\n \"electrophysiology_analysis/barrylab_ephys_analysis/spatial/fields.py\",\n \"electrophysiology_analysis/barrylab_ephys_analysis/lfp/oscillations.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom scipy.stats import pearsonr\nfrom bin_data import bin_data\n\n# import pixel data \nright_z_pixel_change = np.load(\"right_z_pixel_change.npy\")\nleft_z_pixel_change = np.load(\"left_z_pixel_change.npy\")\nfront_z_pixel_change = np.load(\"front_z_pixel_change.npy\")\n\n# average pixel change across front, left & right fovs\npixel_change = np.vstack((left_z_pixel_change, front_z_pixel_change, right_z_pixel_change)).mean(axis=0)\n\n# import rate change data \ndat = pd.read_pickle(\"df_population_vector_change.p\")\n\n# Clean the data (sequential data points are 1cm apart along trajectory)\ndat = dat[dat.environment == 'D']\ndf = dat.filter(['animal', 'x_coord', 'y_coord', 'direction', 'timestamp'], axis=1)\ndat = dat[~df.isnull().any(axis=1)]\ngood_pixel_ids = np.array(np.diff(dat.x_coord)**2 + np.diff(dat.y_coord)**2 < 1.01, dtype=bool)\npixel_change = pixel_change[good_pixel_ids]\ngood_rate_ids = np.append(False, good_pixel_ids)\nturning_rate = np.abs(np.diff(dat['direction'])) % 360\nturning_rate = turning_rate[good_pixel_ids]\ndat = dat[good_rate_ids]\n\n# z-score data\ndat['rate change\\n(euclidean)'] = (dat['rate change\\n(euclidean)'] - np.mean(dat['rate change\\n(euclidean)']))/np.std(dat['rate change\\n(euclidean)'])\npixel_change = (pixel_change - np.mean(pixel_change))/np.std(pixel_change)\n\n# Plot Occupancy\noccupancy = bin_data([dat.x_coord, dat.y_coord], bin_size = 4, limits = [(0, 350), (0, 250)])\nplt.imshow(occupancy.T, origin='upper', cmap=plt.get_cmap('jet'))\nplt.title('Occupancy')\nplt.show()\n\n# Plot pixel change across space\npixel_change_map = bin_data([dat.x_coord, dat.y_coord], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change) / occupancy\nplt.imshow(pixel_change_map.T, origin='upper', cmap=plt.get_cmap('jet'))\nplt.axis('off')\nplt.clim([-1.5,1.5])\nplt.title('Pixel Change Map')\nplt.show()\n\n# Plot firing rate change across space\nrate_change_map = bin_data([dat.x_coord, dat.y_coord], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\n(euclidean)']) / occupancy\nplt.imshow(rate_change_map.T, origin='upper', cmap=plt.get_cmap('jet'))\nplt.axis('off')\nplt.clim([-1.5,1.5])\nplt.title('Rate Change Map')\nplt.show()\n\ncorr, _ = pearsonr(pixel_change, dat['rate change\\n(euclidean)'])\nprint('Rate Change vs Pixel Change Pearson r = %.3f' % corr)\n\n# Filter bits of trajectory by head direction\nnorth_ids = (np.degrees(dat.direction) % 360 >= 315) | (np.degrees(dat.direction) % 360 < 45)\nnorth_occupancy = bin_data([dat.x_coord[north_ids], dat.y_coord[north_ids]], bin_size = 4, limits = [(0, 350), (0, 250)])\nsouth_ids = (np.degrees(dat.direction) % 360 >= 135) & (np.degrees(dat.direction) % 360 < 225)\nsouth_occupancy = bin_data([dat.x_coord[south_ids], dat.y_coord[south_ids]], bin_size = 4, limits = [(0, 350), (0, 250)])\neast_ids = (np.degrees(dat.direction) % 360 >= 45) & (np.degrees(dat.direction) % 360 < 135)\neast_occupancy = bin_data([dat.x_coord[east_ids], dat.y_coord[east_ids]], bin_size = 4, limits = [(0, 350), (0, 250)])\nwest_ids = (np.degrees(dat.direction) % 360 >= 225) & (np.degrees(dat.direction) % 360 < 315)\nwest_occupancy = bin_data([dat.x_coord[west_ids], dat.y_coord[west_ids]], bin_size = 4, limits = [(0, 350), (0, 250)])\n\ncmap = plt.get_cmap('jet')\ncmap.set_bad('w',1.)\n\n# Calculate pixel and rate change maps by heading direction\nnorth_pix_map = bin_data([dat.x_coord[north_ids], dat.y_coord[north_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change[north_ids]) / north_occupancy\nsouth_pix_map = bin_data([dat.x_coord[south_ids], dat.y_coord[south_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change[south_ids]) / south_occupancy\neast_pix_map = bin_data([dat.x_coord[east_ids], dat.y_coord[east_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change[east_ids]) / east_occupancy\nwest_pix_map = bin_data([dat.x_coord[west_ids], dat.y_coord[west_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change[west_ids]) / west_occupancy\nnorth_rat_map = bin_data([dat.x_coord[north_ids], dat.y_coord[north_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\n(euclidean)'][north_ids]) / north_occupancy\nsouth_rat_map = bin_data([dat.x_coord[south_ids], dat.y_coord[south_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\n(euclidean)'][south_ids]) / south_occupancy\neast_rat_map = bin_data([dat.x_coord[east_ids], dat.y_coord[east_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\n(euclidean)'][east_ids]) / east_occupancy\nwest_rat_map = bin_data([dat.x_coord[west_ids], dat.y_coord[west_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\n(euclidean)'][west_ids]) / west_occupancy\n\nc_lo = -1.5\nc_hi = 1.5\n\n# Plot change maps filtered by direction\nplt.subplot(3,3,2)\nplt.title('Unfolded Pixel Change Map')\nplt.imshow(west_pix_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.subplot(3,3,4)\nplt.imshow(south_pix_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.subplot(3,3,5)\nplt.imshow(pixel_change_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.subplot(3,3,6)\nplt.imshow(north_pix_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.subplot(3,3,8)\nplt.imshow(east_pix_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.show()\n\nplt.subplot(3,3,2)\nplt.title('Unfolded Rate Change Map')\nplt.imshow(west_rat_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.subplot(3,3,4)\nplt.imshow(south_rat_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.subplot(3,3,5)\nplt.imshow(rate_change_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.subplot(3,3,6)\nplt.imshow(north_rat_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.subplot(3,3,8)\nplt.imshow(east_rat_map.T, origin='upper', cmap=cmap)\nplt.clim([c_lo,c_hi])\nplt.axis('off')\nplt.show()","\nfrom copy import deepcopy\n\nimport numpy as np\nfrom scipy import ndimage\nimport cv2 as cv\nfrom skimage.measure import regionprops\n\nfrom barrylab_ephys_analysis.spatial.similarity import spatial_correlation\n\n\ndef compute_field_stability(field_ratemap, spike_rates_1, spike_rates_2,\n min_included_value, min_bins):\n \"\"\"Returns the pearson correlation of two ratemaps at location of field ratemap.\n Excludes all bins that are numpy.nan or < 0 in field ratemap.\n\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) array with np.nan outside field\n :param spike_rates_1: shape (n_ybins, n_xbins) array\n :param spike_rates_2: shape (n_ybins, n_xbins) array\n :param min_included_value: minimum value in spike_rates_1 and spike_rates_2 for bin to be included\n :param min_bins: minimum number of bins that must remain to compute correlation, else returns numpy.nan\n :return: rho\n :rtype: float\n \"\"\"\n field_ratemap = field_ratemap.copy()\n field_ratemap[np.isnan(field_ratemap)] = 0\n return spatial_correlation(spike_rates_1, spike_rates_2,\n min_included_value=min_included_value,\n mask=(field_ratemap > 0),\n min_bins=min_bins)[0]\n\n\ndef primary_filter(ratemap, min_area_bins=0, min_peak_value=0):\n \"\"\"Returns True if ratemap passes filter criteria, False otherwise\n\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) array with np.nan outside field\n :param int min_area_bins: number of non-zero bins in ratemap required to pass\n :param float min_peak_value: minimum required maximum value in ratemap to pass\n :return: pass\n :rtype: bool\n \"\"\"\n if np.count_nonzero(ratemap) < min_area_bins:\n return False\n if np.nanmax(ratemap) < min_peak_value:\n return False\n\n return True\n\n\ndef secondary_filter(ratemap, max_area_bins, stability_ratemaps,\n min_stability, stability_kwargs):\n \"\"\"Returns True if ratemap passes filter criteria, False otherwise\n\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) array with np.nan outside field\n :param int max_area_bins: maximum number of bins greater than 0 in ratemap allowed to pass\n :param tuple stability_ratemaps: tuple with two ratemaps that are used for computing the stability\n of a field_ratemap after masking them with the non-numpy.nan elements in field_ratemap\n :param float min_stability: minimum required stability in stability_ratemaps to pass\n :param dict stability_kwargs: passed on to :py:func:`compute_field_stability`\n :return: pass\n :rtype: bool\n \"\"\"\n if np.count_nonzero(ratemap) > max_area_bins:\n return False\n\n if 'min_included_value' in stability_kwargs and stability_kwargs['min_included_value'] <= 0:\n raise Exception('This module uses 0 values as indication of outside of bin areas.\\n'\n + 'Therefore, min_included_value must be above 0 for stability computation,\\n'\n + 'but is currently {}'.format(stability_kwargs['min_included_value']))\n stability = compute_field_stability(ratemap, stability_ratemaps[0],\n stability_ratemaps[1], **stability_kwargs)\n if np.isnan(stability) or stability < min_stability:\n return False\n\n return True\n\n\ndef create_field_ratemap(ratemap, field_idx):\n \"\"\"Returns a copy of input `ratemap`, where fields not True in `field_idx` are set to `0`.\n\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins)\n :param numpy.ndarray field_idx: boolean array same shape as ratemap, specifying which elements\n belong to the field.\n :return: field_ratemap\n :rtype: numpy.ndarray\n \"\"\"\n field_ratemap = ratemap.copy()\n field_ratemap[~field_idx] = 0\n\n return field_ratemap\n\n\ndef get_field_map(ratemap):\n \"\"\"Returns the contiguous region map as identified with :py:func:`scipy.ndimage.label`.\n\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins)\n :return: same as input but integer identifiers marking which elements belong to which proximal group\n :rtype: numpy.ndarray\n \"\"\"\n return ndimage.label(ratemap > 0)[0]\n\n\ndef get_filtered_subfield_ratemaps(ratemap, primary_filter_kwargs):\n \"\"\"Returns a list containing a copy of ratemap for each field that passes the primary filter\n :py:func:`spatial.fields.primary_filter` where values outside the field are set `0`.\n\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins)\n :param dict primary_filter_kwargs: see :py:func:`spatial.field.primary_filter` keyword arguments.\n filter_kwargs is passed on to that function as `**filter_kwargs` after `ratemap` argument.\n :return: field_ratemaps\n :rtype: list\n \"\"\"\n field_map = get_field_map(ratemap)\n\n field_nrs = np.unique(field_map)[1:] # ignores the 0 background field\n\n # If no fields were detected, return no fields\n if len(field_nrs) == 0:\n return []\n\n field_ratemaps = []\n for field_nr in field_nrs:\n field_ratemap = create_field_ratemap(ratemap, field_map == field_nr)\n if primary_filter(field_ratemap, **primary_filter_kwargs):\n field_ratemaps.append(field_ratemap)\n\n return field_ratemaps\n\n\ndef detect_field_candidates(ratemap, base_threshold, threshold_step, primary_filter_kwargs):\n \"\"\"Returns a list of field_candidates that passed primary_filter.\n\n The returned field_candidates list is a nested list with the following structure:\n - Each element in list contains two elements.\n - The first element is the origin index within field_candidates. This is None for first subfields,\n but int after.\n - The second element is a list of ratemaps with increasingly higher threshold, that all\n have only a single continugous region. The final element in the ratemap list is None,\n if no further subfields were found with the next threshold (none passed the primary_filter).\n - If more than one subfield is found, these are separately appended to field_candidates list following\n the same structure and using the origin index of the previous level where they were detected.\n\n - The field_candidates list is populated in an iterative fashion. Each iteration the ratemap threshold is increase\n by threshold_step.\n - If a single new field passes primary_filter, it is appended to the list of ratemaps for that level and\n procedure is repeated.\n - If no new fields pass primary_filter, the ratemap list is ended with None. This causes the loop to start\n the procedure on the next level in the field_candidates list.\n - New levels are added to the field_candidates list each time two or more fields are detected and pass the\n primary_filter. In that case, after the new fields are appended to field_candidates list,\n the loop moves on to next level.\n - As a result the loop extracts all possible field_candidates and also forms links between them.\n This allows working backwards through this list along the order of inheritance (field and subfield links).\n\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) ratemap. Any numpy.nan elements should\n be replaced with zeros before passing ratemap to this function\n :param float base_threshold: baseline threshold level from which to start detecting fields\n :param float threshold_step: threshold shift at each iteration\n :param dict primary_filter_kwargs: see :py:func:`spatial.field.primary_filter` keyword arguments.\n filter_kwargs is passed on to that function as `**filter_kwargs` after `ratemap` argument.\n :return: field_candidates\n :rtype: list\n \"\"\"\n\n # Use a copy of original input ratemap, threshold and threshold the ratemap.\n ratemap = ratemap.copy()\n current_threshold = deepcopy(base_threshold)\n ratemap[ratemap < current_threshold] = 0\n\n # Find all subfields in the ratemap at baseline threshold\n subfields = get_filtered_subfield_ratemaps(ratemap, primary_filter_kwargs)\n\n # If no subfields were found, return an empty list\n if len(subfields) == 0:\n return []\n\n # Create fields list with the initial subfields\n field_candidates = []\n for subfield in subfields:\n field_candidates.append((None, [subfield]))\n\n current_level = 0\n\n # Continuoue the loop until the last level ratemap list ends with None\n while not (field_candidates[-1][1][-1] is None):\n\n # If current level ratemaplist ends with None, move to next level\n if field_candidates[current_level][1][-1] is None:\n current_level += 1\n\n # Increase current_threshold and create a copy of current_ratemap with the current_threshold\n current_threshold += threshold_step\n current_ratemap = field_candidates[current_level][1][-1].copy()\n current_ratemap[current_ratemap < current_threshold] = 0\n\n # Find subfields for current level\n subfields = get_filtered_subfield_ratemaps(current_ratemap, primary_filter_kwargs)\n\n if len(subfields) == 0:\n # If no subfields were found, end the current level ratemap list with None\n field_candidates[current_level][1].append(None)\n\n elif len(subfields) == 1:\n # If a single field was found, append to current level ratemap list\n field_candidates[current_level][1].append(subfields[0])\n\n else:\n # If more than one field was found, append these to field_candidates list\n for subfield in subfields:\n field_candidates.append((current_level, [subfield]))\n # If more than one field was found, move to next level\n current_level += 1\n\n return field_candidates\n\n\ndef extract_fields_from_field_candidates(field_candidates, secondary_filter_kwargs):\n \"\"\"Returns the field_ratemap with lowest threshold of each field_candidate that\n passes the :py:func:`secondary_filter`.\n\n Iterates through field_candidates in reverse order of detection in :py:func:`detect_field_candidates`.\n Ignores any field_candiate elements that were the origin of sub-fields that pass secondary_filter.\n\n :param list field_candidates: output from :py:func:`detect_field_candidates`\n :param dict secondary_filter_kwargs: see :py:func:`spatial.field.secondary_filter` keyword arguments.\n filter_kwargs is passed on to that function as `**filter_kwargs` after `ratemap` argument.\n :return: field_ratemaps\n :rtype: list\n \"\"\"\n field_ratemap_dicts = []\n\n # Loop through levels of the field_candidates list starting from the last\n for current_level in range(len(field_candidates))[::-1]:\n\n # Get the field_candidate element for the current_level\n field_candidate = field_candidates[current_level]\n\n # Find out if current level field_candidate has any sub-fields already passed the secondary_filter\n n_subfields = 0\n for field_ratemap_dict in field_ratemap_dicts:\n if field_ratemap_dict['origin'] == current_level:\n n_subfields += 1\n\n # If more than one subfield has been identified for this field_candidate,\n # pass origin of this field_candidate to detected subfields and skip processing this field_candidate.\n if n_subfields > 1:\n for field_ratemap_dict in field_ratemap_dicts:\n if field_ratemap_dict['origin'] == current_level:\n field_ratemap_dict['origin'] = field_candidate[0]\n continue\n\n field_ratemap = None\n\n # Loop through the ratemaps of this field_candiate\n for field_candidate_ratemap in field_candidate[1][::-1]:\n\n # field_candidate_ratemap lists can end in None. Ignore these elements.\n if field_candidate_ratemap is None:\n continue\n\n if secondary_filter(field_candidate_ratemap, **secondary_filter_kwargs):\n # If a ratemap passes the secondary_filter, overwrite the field_ratemap\n # This way final field_ratemap is the one detected with lowest threshold\n # but still passes the secondary_filter.\n field_ratemap = field_candidate_ratemap\n\n if field_ratemap is None:\n\n if n_subfields == 1:\n # If no field_ratemap passed the secondary_filter fo this field_candidate,\n # but this field_candidate had one subfield passing through the filter earlier\n # assign the origin of that subfield to be the origin of current field_candidate.\n subfield_index = [field_ratemap_dict['origin']\n for field_ratemap_dict in field_ratemap_dicts].index(current_level)\n field_ratemap_dicts[subfield_index]['origin'] = field_candidate[0]\n\n else:\n\n # If a field_ratemap did pass the secondary_filter, append it to field_ratemap_dicts\n field_ratemap_dicts.append({'ratemap': field_ratemap,\n 'origin': field_candidate[0]})\n\n if n_subfields == 1:\n # Remove the single subfield of the current field_candidate from field_ratemap_dicts list\n subfield_index = [field_ratemap_dict['origin']\n for field_ratemap_dict in field_ratemap_dicts].index(current_level)\n del field_ratemap_dicts[subfield_index]\n\n field_ratemaps = [field_ratemap_dict['ratemap'] for field_ratemap_dict in field_ratemap_dicts\n if 'ratemap' in field_ratemap_dict]\n\n return field_ratemaps\n\n\ndef detect_fields(ratemap, stability_ratemaps, base_threshold, threshold_step,\n primary_filter_kwargs, secondary_filter_kwargs):\n \"\"\"Returns a list of copies of input `ratemap` with every value except those in field\n replaced by `numpy.nan`.\n\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) ratemap for fields to be detected.\n Values to be ignored should be set to numpy.nan.\n :param tuple stability_ratemaps: tuple with two ratemaps that are used for computing the stability\n of a field_ratemap after masking them with the non-numpy.nan elements in field_ratemap\n :param float base_threshold: values below this are set to numpy.nan and ignored\n :param float threshold_step: the step in ratemap values for iterative detection of fields.\n :param dict primary_filter_kwargs: see :py:func:`spatial.field.primary_filter` keyword arguments.\n filter_kwargs is passed on to that function as `**filter_kwargs` after `ratemap` argument.\n :param dict secondary_filter_kwargs: see :py:func:`spatial.field.secondary_filter` keyword arguments.\n `'max_relative_bins'` element is replaced with `'max_area_bins'` computed based on the `ratemap`.\n secondary_filter_kwargs is then passed on to that function as `**filter_kwargs` after `ratemap` argument.\n :return: field_ratemaps list of ratemaps where values outside field are numpy.nan\n :rtype: list\n \"\"\"\n # Add stability_ratemaps and max_area_bins to a copy of secondary_filter_kwargs\n secondary_filter_kwargs = deepcopy(secondary_filter_kwargs)\n secondary_filter_kwargs['stability_ratemaps'] = stability_ratemaps\n secondary_filter_kwargs['max_area_bins'] = \\\n np.sum(~np.isnan(ratemap)) * secondary_filter_kwargs.pop('max_relative_bins')\n\n # Ensure original ratemap is not modified\n ratemap = ratemap.copy()\n\n # Ensure ratemap is in float dtype to support nans that are required in final output format\n if not isinstance(ratemap.dtype, np.floating):\n ratemap = ratemap.astype(np.float64)\n\n # Set numpy.nan values to 0 for compatibility with field detection methods.\n ratemap[np.isnan(ratemap)] = 0\n\n # Detect field candidates and extract those that pass all filters\n field_candidates = detect_field_candidates(ratemap, base_threshold, threshold_step, primary_filter_kwargs)\n field_ratemaps = extract_fields_from_field_candidates(field_candidates, secondary_filter_kwargs)\n\n # Set field_ratemap values of 0 to numpy.nan to indicate outside of field areas\n for i, field_ratemap in enumerate(field_ratemaps):\n field_ratemaps[i][field_ratemap == 0] = np.nan\n\n return field_ratemaps\n\n\ndef get_field_regionprops(field_ratemap, data_x_values=None, data_y_values=None,\n orientation_x_axis_0_to_pi=True):\n \"\"\"Returns output from :py:func:`skimage.measure.regionprops` for the boolean field map.\n\n Note, if data_x_values and data_y_values (both or neither must be provided), these are used\n to estimate corresponding centroid_x, centroid_y, minor_axis and major_axis values.\n\n Note, data_x_values and data_y_values must have equal and constant spacing in values,\n as the correction on minor_axis and major_axis length are based on this assumption.\n\n :param numpy.ndarray field_ratemap: shape (n_ybins, n_xbins) ratemap with numpy.nan values\n outside the field.\n :param numpy.ndarray data_x_values: if provided, to estimate position and axes length values\n :param numpy.ndarray data_y_values: if provided, to estimate position and axes length values\n :param bool orientation_x_axis_0_to_pi: if True (default), orientation value is set to range from\n 0 to pi, increasing clock-wise from parallel to x-axis.\n :return: dictionary with selection of field properties returned by :py:func:`skimage.measure.regionprops`\n :rtype: dict\n \"\"\"\n if (data_x_values is None) != (data_y_values is None):\n raise ValueError('data_x_values and data_y_values must both be provided or neither.')\n\n if not (data_x_values is None):\n x_offset = data_x_values[0]\n y_offset = data_y_values[0]\n pixel_edge_length = data_x_values[1] - data_x_values[0]\n else:\n x_offset = None\n y_offset = None\n pixel_edge_length = None\n\n out = regionprops(np.uint8(~np.isnan(field_ratemap)), coordinates='rc')[0]\n return {'area': out.area,\n 'centroid_x': (out.centroid[1] if (data_x_values is None)\n else out.centroid[1] * pixel_edge_length + x_offset),\n 'centroid_y': (out.centroid[0] if (data_y_values is None)\n else out.centroid[0] * pixel_edge_length + y_offset),\n 'eccentricity': out.eccentricity,\n 'minor_axis': (out.minor_axis_length if pixel_edge_length is None\n else out.minor_axis_length * pixel_edge_length),\n 'major_axis': (out.major_axis_length if pixel_edge_length is None\n else out.major_axis_length * pixel_edge_length),\n 'orientation': ((-out.orientation + np.pi / 2.) if orientation_x_axis_0_to_pi\n else out.orientation)}\n\n\ndef compute_field_contour(field_ratemap, flipud=True):\n \"\"\"Returns contour points of the field in the field_ratemap.\n\n Assumes input has only one contiguous region of values above 0 and not `numpy.nan`,\n\n :param numpy.ndarray field_ratemap: shape (n_ybins, n_xbins) ratemap with numpy.nan values\n outside the field.\n :return: contours list of contour points\n :param bool flipud: if True (default), the field_ratemap array is flipped prior to extracting\n the contour. This yields correct contour point values relative to field_ratemap array indices.\n :rtype: list\n \"\"\"\n # The ratemap needs to be flipped on the vertical axis, because OpenCV starts counting\n # y-axis positions from bottom edge, but our analysis assumes position values are counted\n # ascending from the top edge of the ratemap array.\n if flipud:\n field_ratemap = np.flipud(field_ratemap.copy())\n\n # Convert input to binary\n field_ratemap[np.isnan(field_ratemap)] = 0\n field_binary_map = np.uint8(field_ratemap > 0)\n\n # Only keep the first contour from output list, as input should only have one region\n contour = cv.findContours(field_binary_map, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)[0][0]\n\n return contour\n\n\ndef compute_field_ellipse(field_ratemap, flipud=False):\n \"\"\"\n\n :param numpy.ndarray field_ratemap: shape (n_ybins, n_xbins) ratemap with numpy.nan values\n outside the field.\n :param bool flipud: if False (default), ratemap is not flipped along first dimension for\n detecting contour prior to ellipse fitting. This yields correct ellipse coordinates\n relative to field_ratemap array indices.\n :return:\n \"\"\"\n\n contour = compute_field_contour(field_ratemap, flipud=flipud)\n\n (x, y), (MA, ma), angle = cv.fitEllipse(contour)\n\n return {'x': x, 'y': y, 'major_axis_width': MA, 'minor_axis_width': ma, 'angle': angle}\n","\nimport numpy as np\n\nfrom barrylab_ephys_analysis.blea_utils import smooth_signal_with_gaussian\nfrom barrylab_ephys_analysis.lfp.processing import bandpass_filter, hilbert_fast\nfrom barrylab_ephys_analysis.base import AnalogSignal\n\n\nclass FrequencyBandAmplitude(AnalogSignal):\n \"\"\"\n Computes frequency band amplitude for a recording and provides convenient access based on\n :py:class:`barrylab_ephys_analysis.base.AnalogSignal`.\n\n If existing data with same name is found in recording.analysis['analog_signals'],\n then that data is used instead, if it matches the input parameters.\n \"\"\"\n\n def __init__(self, *args, highpass_frequency=None, lowpass_frequency=None,\n filter_order=None, temporal_smoothing_sigma=None, common_average=False,\n channel_group_tetrodes=None, verbose=False, **kwargs):\n \"\"\"\n :param highpass_frequency: Hz (must be provided)\n :param lowpass_frequency: Hz (must be provided)\n :param int filter_order: (must be provided)\n :param float temporal_smoothing_sigma: temporal smoothing gaussian stdev in seconds (must be provided)\n :param bool common_average: specifies if common average is compute for each channel group before processing.\n Default is False, no common averaging.\n :param dict channel_group_tetrodes: {channel_group_A: 5, channel_group_B: [6, 8]} specifies that for\n channel_group_A, only tetrode 5 is used and for channel_group_B, tetrodes 6 and 8 are used.\n Default is to use all tetrodes on all channel groups. This must be provided for all groups or none.\n :param bool verbose: if True, prints progress. Default is False.\n \"\"\"\n self._params = {\n 'highpass_frequency': np.array(highpass_frequency),\n 'lowpass_frequency': np.array(lowpass_frequency),\n 'filter_order': np.array(filter_order),\n 'temporal_smoothing_sigma': np.array(temporal_smoothing_sigma),\n 'common_average': np.array(common_average),\n 'channel_group_tetrodes': ({key: np.array(value) for key, value in channel_group_tetrodes.items()}\n if isinstance(channel_group_tetrodes, dict) else np.array(np.nan))\n }\n self.verbose = verbose\n super(FrequencyBandAmplitude, self).__init__(*args, **kwargs)\n\n @staticmethod\n def compute_frequency_band_amplitude(continuous, sampling_rate, highpass_frequency,\n lowpass_frequency, filter_order):\n \"\"\"Returns amplitude envelope of a signal in 1-D array\n\n :param numpy.ndarray continuous: shape (N,)\n :param sampling_rate: Hz\n :param highpass_frequency: Hz\n :param lowpass_frequency: Hz\n :param int filter_order:\n :return: amplitude_envelope\n :rtype: numpy.ndarray\n \"\"\"\n filtered_signal = bandpass_filter(continuous, sampling_rate=sampling_rate,\n lowpass_frequency=lowpass_frequency,\n highpass_frequency=highpass_frequency,\n filter_order=filter_order)\n return np.abs(hilbert_fast(filtered_signal))\n\n def compute_values_for_continuous_array(self, continuous):\n \"\"\"Computes frequency band amplitude for along first dimension for all columns.\n\n This can be overwritten by a subclass to provide a different value for continuous array,\n but must return an array of same shape as it receives.\n\n Note! The output from this function is averaged along the second dimension, using np.mean(x, axis=1).\n\n :param numpy.ndarray continuous: shape (n_samples, n_channels)\n :return: value_array shape (n_samples, n_channels)\n :rtype: numpy.ndarray\n \"\"\"\n return np.apply_along_axis(\n FrequencyBandAmplitude.compute_frequency_band_amplitude, 0, continuous,\n self.recording.continuous['sampling_rate'],\n highpass_frequency=self._params['highpass_frequency'],\n lowpass_frequency=self._params['lowpass_frequency'],\n filter_order=self._params['filter_order']\n )\n\n @staticmethod\n def smooth_signal(signal, temporal_smoothing_sigma, sampling_rate):\n return smooth_signal_with_gaussian(signal, temporal_smoothing_sigma * sampling_rate)\n\n def compute_for_channel_group(self, channel_group):\n\n if isinstance(self._params['channel_group_tetrodes'], dict):\n tetrode_nrs = self._params['channel_group_tetrodes'][channel_group]\n else:\n tetrode_nrs = self.recording.channel_map[channel_group]['tetrode_nrs']\n\n if self._params['common_average'] and len(tetrode_nrs) > 1:\n\n common_average = np.mean(self.recording.continuous['continuous'][:, tetrode_nrs], axis=1)\n continuous = np.apply_along_axis(lambda x: x - common_average, 0,\n self.recording.continuous['continuous'][:, tetrode_nrs])\n else:\n\n continuous = self.recording.continuous['continuous'][:, tetrode_nrs]\n\n if len(continuous.shape) == 1:\n continuous = continuous[:, np.newaxis]\n\n amplitude = np.mean(self.compute_values_for_continuous_array(continuous), axis=1)\n\n if (not (self._params['temporal_smoothing_sigma'] is None)\n and self._params['temporal_smoothing_sigma'] != 0):\n amplitude = self.smooth_signal(amplitude, self._params['temporal_smoothing_sigma'],\n self.recording.continuous['sampling_rate'])\n\n return amplitude\n\n def compute(self, recompute=False):\n\n # If available data params match input params, skip recomputing, unless requested\n if not recompute and not (self.data is None):\n params_match = []\n for key, item in self._params.items():\n if key == 'channel_group_tetrodes' and not isinstance(item, dict):\n continue\n params_match.append(self.data['params'][key] == item)\n if all(params_match):\n if self.verbose:\n print('Exisiting {} params match input, skipping computation.'.format(self.name))\n return\n\n if self.verbose:\n print('Computing {}'.format(self.name))\n\n signals = []\n labels = []\n\n for channel_group in self.recording.channel_map:\n signals.append(self.compute_for_channel_group(channel_group)[:, None])\n labels.append(channel_group)\n\n self.data = {'first_timestamp': np.array(self.recording.continuous['timestamps'][0]),\n 'sampling_rate': np.array(self.recording.continuous['sampling_rate']),\n 'signal': np.concatenate(signals, axis=1),\n 'channel_labels': labels,\n 'params': self._params}\n\n\nclass FrequencyBandFrequency(FrequencyBandAmplitude):\n \"\"\"\n Computes frequency band instantaneous frequency for a recording and provides convenient access based on\n :py:class:`FrequencyBandAmplitude` and :py:class:`barrylab_ephys_analysis.base.AnalogSignal`.\n \"\"\"\n\n @staticmethod\n def compute_frequency_band_frequency(continuous, sampling_rate, highpass_frequency,\n lowpass_frequency, filter_order):\n \"\"\"Returns instantaneous frequency of a signal in 1-D array.\n\n Note! Due to the nature of computing instantaneous frequency, the values are\n shifted forward by half a sample.\n\n :param numpy.ndarray continuous: shape (N,)\n :param sampling_rate: Hz\n :param highpass_frequency: Hz\n :param lowpass_frequency: Hz\n :param int filter_order:\n :return: instantaneous_frequency shape (N,)\n :rtype: numpy.ndarray\n \"\"\"\n filtered_signal = bandpass_filter(continuous, sampling_rate=sampling_rate,\n lowpass_frequency=lowpass_frequency,\n highpass_frequency=highpass_frequency,\n filter_order=filter_order)\n instantaneous_phase = np.unwrap(np.angle(hilbert_fast(filtered_signal)))\n instantaneous_frequency = (np.diff(instantaneous_phase) / (2.0 * np.pi) * sampling_rate)\n instantaneous_frequency = np.concatenate((instantaneous_frequency[:1], instantaneous_frequency))\n return instantaneous_frequency\n\n def compute_values_for_continuous_array(self, continuous):\n \"\"\"Computes instantaneous frequency along first dimension for all columns.\n\n :param numpy.ndarray continuous: shape (n_samples, n_channels)\n :return: value_array shape (n_samples, n_channels)\n :rtype: numpy.ndarray\n \"\"\"\n return np.apply_along_axis(\n FrequencyBandFrequency.compute_frequency_band_frequency, 0, continuous,\n self.recording.continuous['sampling_rate'],\n highpass_frequency=self._params['highpass_frequency'],\n lowpass_frequency=self._params['lowpass_frequency'],\n filter_order=self._params['filter_order']\n )\n\n\nclass FrequencyBandPhase(FrequencyBandAmplitude):\n \"\"\"\n Computes frequency band instantaneous phase for a recording and provides convenient access based on\n :py:class:`FrequencyBandAmplitude` and :py:class:`barrylab_ephys_analysis.base.AnalogSignal`.\n\n Phase is computed and stored in radians in unwrapped form.\n\n .. warning::\n\n If temporal_smoothing_sigma argument above 0 is provided, then phase is unwrapped using\n :py:func:`numpy.unwrap` and wrapped again using :py:func:`FrequencyBandPhase.wrap_phase_to_range_between_pi`.\n It is important that the sampling rate of the signal is sufficiently high for this method to work.\n\n \"\"\"\n\n @staticmethod\n def wrap_phase_to_range_between_pi(signal):\n \"\"\"Returns input signal wrapped to range between -pi to pi\n\n :param numpy.ndarray signal:\n :return: wrapped_signal\n \"\"\"\n return np.mod(signal + np.pi, 2 * np.pi) - np.pi\n\n @staticmethod\n def compute_frequency_band_phase(continuous, sampling_rate, highpass_frequency,\n lowpass_frequency, filter_order):\n \"\"\"Returns instantaneous phase of a signal in 1-D array in unwrapped form.\n\n :param numpy.ndarray continuous: shape (N,)\n :param sampling_rate: Hz\n :param highpass_frequency: Hz\n :param lowpass_frequency: Hz\n :param int filter_order:\n :return: instantaneous_phase shape (N,)\n :rtype: numpy.ndarray\n \"\"\"\n filtered_signal = bandpass_filter(continuous, sampling_rate=sampling_rate,\n lowpass_frequency=lowpass_frequency,\n highpass_frequency=highpass_frequency,\n filter_order=filter_order)\n instantaneous_phase = np.angle(hilbert_fast(filtered_signal))\n return instantaneous_phase\n\n def compute_values_for_continuous_array(self, continuous):\n \"\"\"Computes instantaneous phase along first dimension for all columns.\n\n :param numpy.ndarray continuous: shape (n_samples, n_channels)\n :return: value_array shape (n_samples, n_channels)\n :rtype: numpy.ndarray\n \"\"\"\n return np.apply_along_axis(\n FrequencyBandPhase.compute_frequency_band_phase, 0, continuous,\n self.recording.continuous['sampling_rate'],\n highpass_frequency=self._params['highpass_frequency'],\n lowpass_frequency=self._params['lowpass_frequency'],\n filter_order=self._params['filter_order']\n )\n\n @staticmethod\n def smooth_signal(signal, temporal_smoothing_sigma, sampling_rate):\n return FrequencyBandPhase.wrap_phase_to_range_between_pi(\n smooth_signal_with_gaussian(np.unwrap(signal, axis=0), temporal_smoothing_sigma * sampling_rate)\n )\n\n\nclass ThetaDeltaRatio(AnalogSignal):\n\n def __init__(self, *args, theta_band_amplitude=None, delta_band_amplitude=None,\n speed=None, mean_ratio_min_speed=None, **kwargs):\n \"\"\"\n :param theta_band_amplitude: :py:class:`FrequencyBandAmplitude` instantiated for theta frequency band\n :param delta_band_amplitude: :py:class:`FrequencyBandAmplitude` instantiated for delta frequency band\n :param speed: :py:class:`barrylab_ephys_analysis.base.AnalogSignal` for animal speed\n :param float mean_ratio_min_speed: minimum animal speed threshold for calculating mean theta/delta ratio\n \"\"\"\n self._theta_band_amplitude = theta_band_amplitude\n self._delta_band_amplitude = delta_band_amplitude\n self._speed = speed\n self._params = {'mean_ratio_min_speed': np.array(mean_ratio_min_speed)}\n super(ThetaDeltaRatio, self).__init__(*args, **kwargs)\n\n def compute(self, recompute=False):\n\n # If available data params match input params, skip recomputing, unless requested\n if not recompute and not (self.data is None):\n if all([self.data['params'][key] == item for key, item in self._params.items()]):\n return\n\n print('Computing ThetaDeltaRatio')\n\n # Compute mean np.log(ratio) of theta/delta amplitude during running epochs\n running_epoch_timestamps = self._speed.get_epoch_timestamps_function_of_signal_true(\n lambda x: np.array(x > self._params['mean_ratio_min_speed']).squeeze()\n )\n running_theta_amplitude = np.mean(\n np.concatenate(\n [self._theta_band_amplitude.get_values_between_timestamps(*ets, allow_clipping=True)[0]\n for ets in running_epoch_timestamps]\n ),\n axis=1\n )\n running_delta_amplitude = np.mean(\n np.concatenate(\n [self._delta_band_amplitude.get_values_between_timestamps(*ets, allow_clipping=True)[0]\n for ets in running_epoch_timestamps]\n ),\n axis=1\n )\n running_mean_ratio = np.mean(np.log(running_theta_amplitude / running_delta_amplitude))\n\n # Get theta/delta ratio with timestamps\n ratio = np.log(np.mean(self._theta_band_amplitude.data['signal'], axis=1)\n / np.mean(self._delta_band_amplitude.data['signal'], axis=1))\n\n self.data = {'first_timestamp': self._theta_band_amplitude.data['first_timestamp'],\n 'sampling_rate': self._theta_band_amplitude.data['sampling_rate'],\n 'signal': ratio[:, None],\n 'channel_labels': [''],\n 'params': self._params,\n 'running_mean_ratio': np.array(running_mean_ratio)}\n"],"string":"[\n \"import numpy as np\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nfrom scipy.stats import pearsonr\\nfrom bin_data import bin_data\\n\\n# import pixel data \\nright_z_pixel_change = np.load(\\\"right_z_pixel_change.npy\\\")\\nleft_z_pixel_change = np.load(\\\"left_z_pixel_change.npy\\\")\\nfront_z_pixel_change = np.load(\\\"front_z_pixel_change.npy\\\")\\n\\n# average pixel change across front, left & right fovs\\npixel_change = np.vstack((left_z_pixel_change, front_z_pixel_change, right_z_pixel_change)).mean(axis=0)\\n\\n# import rate change data \\ndat = pd.read_pickle(\\\"df_population_vector_change.p\\\")\\n\\n# Clean the data (sequential data points are 1cm apart along trajectory)\\ndat = dat[dat.environment == 'D']\\ndf = dat.filter(['animal', 'x_coord', 'y_coord', 'direction', 'timestamp'], axis=1)\\ndat = dat[~df.isnull().any(axis=1)]\\ngood_pixel_ids = np.array(np.diff(dat.x_coord)**2 + np.diff(dat.y_coord)**2 < 1.01, dtype=bool)\\npixel_change = pixel_change[good_pixel_ids]\\ngood_rate_ids = np.append(False, good_pixel_ids)\\nturning_rate = np.abs(np.diff(dat['direction'])) % 360\\nturning_rate = turning_rate[good_pixel_ids]\\ndat = dat[good_rate_ids]\\n\\n# z-score data\\ndat['rate change\\\\n(euclidean)'] = (dat['rate change\\\\n(euclidean)'] - np.mean(dat['rate change\\\\n(euclidean)']))/np.std(dat['rate change\\\\n(euclidean)'])\\npixel_change = (pixel_change - np.mean(pixel_change))/np.std(pixel_change)\\n\\n# Plot Occupancy\\noccupancy = bin_data([dat.x_coord, dat.y_coord], bin_size = 4, limits = [(0, 350), (0, 250)])\\nplt.imshow(occupancy.T, origin='upper', cmap=plt.get_cmap('jet'))\\nplt.title('Occupancy')\\nplt.show()\\n\\n# Plot pixel change across space\\npixel_change_map = bin_data([dat.x_coord, dat.y_coord], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change) / occupancy\\nplt.imshow(pixel_change_map.T, origin='upper', cmap=plt.get_cmap('jet'))\\nplt.axis('off')\\nplt.clim([-1.5,1.5])\\nplt.title('Pixel Change Map')\\nplt.show()\\n\\n# Plot firing rate change across space\\nrate_change_map = bin_data([dat.x_coord, dat.y_coord], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\\\n(euclidean)']) / occupancy\\nplt.imshow(rate_change_map.T, origin='upper', cmap=plt.get_cmap('jet'))\\nplt.axis('off')\\nplt.clim([-1.5,1.5])\\nplt.title('Rate Change Map')\\nplt.show()\\n\\ncorr, _ = pearsonr(pixel_change, dat['rate change\\\\n(euclidean)'])\\nprint('Rate Change vs Pixel Change Pearson r = %.3f' % corr)\\n\\n# Filter bits of trajectory by head direction\\nnorth_ids = (np.degrees(dat.direction) % 360 >= 315) | (np.degrees(dat.direction) % 360 < 45)\\nnorth_occupancy = bin_data([dat.x_coord[north_ids], dat.y_coord[north_ids]], bin_size = 4, limits = [(0, 350), (0, 250)])\\nsouth_ids = (np.degrees(dat.direction) % 360 >= 135) & (np.degrees(dat.direction) % 360 < 225)\\nsouth_occupancy = bin_data([dat.x_coord[south_ids], dat.y_coord[south_ids]], bin_size = 4, limits = [(0, 350), (0, 250)])\\neast_ids = (np.degrees(dat.direction) % 360 >= 45) & (np.degrees(dat.direction) % 360 < 135)\\neast_occupancy = bin_data([dat.x_coord[east_ids], dat.y_coord[east_ids]], bin_size = 4, limits = [(0, 350), (0, 250)])\\nwest_ids = (np.degrees(dat.direction) % 360 >= 225) & (np.degrees(dat.direction) % 360 < 315)\\nwest_occupancy = bin_data([dat.x_coord[west_ids], dat.y_coord[west_ids]], bin_size = 4, limits = [(0, 350), (0, 250)])\\n\\ncmap = plt.get_cmap('jet')\\ncmap.set_bad('w',1.)\\n\\n# Calculate pixel and rate change maps by heading direction\\nnorth_pix_map = bin_data([dat.x_coord[north_ids], dat.y_coord[north_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change[north_ids]) / north_occupancy\\nsouth_pix_map = bin_data([dat.x_coord[south_ids], dat.y_coord[south_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change[south_ids]) / south_occupancy\\neast_pix_map = bin_data([dat.x_coord[east_ids], dat.y_coord[east_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change[east_ids]) / east_occupancy\\nwest_pix_map = bin_data([dat.x_coord[west_ids], dat.y_coord[west_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = pixel_change[west_ids]) / west_occupancy\\nnorth_rat_map = bin_data([dat.x_coord[north_ids], dat.y_coord[north_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\\\n(euclidean)'][north_ids]) / north_occupancy\\nsouth_rat_map = bin_data([dat.x_coord[south_ids], dat.y_coord[south_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\\\n(euclidean)'][south_ids]) / south_occupancy\\neast_rat_map = bin_data([dat.x_coord[east_ids], dat.y_coord[east_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\\\n(euclidean)'][east_ids]) / east_occupancy\\nwest_rat_map = bin_data([dat.x_coord[west_ids], dat.y_coord[west_ids]], bin_size = 4, limits = [(0, 350), (0, 250)], var_to_bin = dat['rate change\\\\n(euclidean)'][west_ids]) / west_occupancy\\n\\nc_lo = -1.5\\nc_hi = 1.5\\n\\n# Plot change maps filtered by direction\\nplt.subplot(3,3,2)\\nplt.title('Unfolded Pixel Change Map')\\nplt.imshow(west_pix_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.subplot(3,3,4)\\nplt.imshow(south_pix_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.subplot(3,3,5)\\nplt.imshow(pixel_change_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.subplot(3,3,6)\\nplt.imshow(north_pix_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.subplot(3,3,8)\\nplt.imshow(east_pix_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.show()\\n\\nplt.subplot(3,3,2)\\nplt.title('Unfolded Rate Change Map')\\nplt.imshow(west_rat_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.subplot(3,3,4)\\nplt.imshow(south_rat_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.subplot(3,3,5)\\nplt.imshow(rate_change_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.subplot(3,3,6)\\nplt.imshow(north_rat_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.subplot(3,3,8)\\nplt.imshow(east_rat_map.T, origin='upper', cmap=cmap)\\nplt.clim([c_lo,c_hi])\\nplt.axis('off')\\nplt.show()\",\n \"\\nfrom copy import deepcopy\\n\\nimport numpy as np\\nfrom scipy import ndimage\\nimport cv2 as cv\\nfrom skimage.measure import regionprops\\n\\nfrom barrylab_ephys_analysis.spatial.similarity import spatial_correlation\\n\\n\\ndef compute_field_stability(field_ratemap, spike_rates_1, spike_rates_2,\\n min_included_value, min_bins):\\n \\\"\\\"\\\"Returns the pearson correlation of two ratemaps at location of field ratemap.\\n Excludes all bins that are numpy.nan or < 0 in field ratemap.\\n\\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) array with np.nan outside field\\n :param spike_rates_1: shape (n_ybins, n_xbins) array\\n :param spike_rates_2: shape (n_ybins, n_xbins) array\\n :param min_included_value: minimum value in spike_rates_1 and spike_rates_2 for bin to be included\\n :param min_bins: minimum number of bins that must remain to compute correlation, else returns numpy.nan\\n :return: rho\\n :rtype: float\\n \\\"\\\"\\\"\\n field_ratemap = field_ratemap.copy()\\n field_ratemap[np.isnan(field_ratemap)] = 0\\n return spatial_correlation(spike_rates_1, spike_rates_2,\\n min_included_value=min_included_value,\\n mask=(field_ratemap > 0),\\n min_bins=min_bins)[0]\\n\\n\\ndef primary_filter(ratemap, min_area_bins=0, min_peak_value=0):\\n \\\"\\\"\\\"Returns True if ratemap passes filter criteria, False otherwise\\n\\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) array with np.nan outside field\\n :param int min_area_bins: number of non-zero bins in ratemap required to pass\\n :param float min_peak_value: minimum required maximum value in ratemap to pass\\n :return: pass\\n :rtype: bool\\n \\\"\\\"\\\"\\n if np.count_nonzero(ratemap) < min_area_bins:\\n return False\\n if np.nanmax(ratemap) < min_peak_value:\\n return False\\n\\n return True\\n\\n\\ndef secondary_filter(ratemap, max_area_bins, stability_ratemaps,\\n min_stability, stability_kwargs):\\n \\\"\\\"\\\"Returns True if ratemap passes filter criteria, False otherwise\\n\\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) array with np.nan outside field\\n :param int max_area_bins: maximum number of bins greater than 0 in ratemap allowed to pass\\n :param tuple stability_ratemaps: tuple with two ratemaps that are used for computing the stability\\n of a field_ratemap after masking them with the non-numpy.nan elements in field_ratemap\\n :param float min_stability: minimum required stability in stability_ratemaps to pass\\n :param dict stability_kwargs: passed on to :py:func:`compute_field_stability`\\n :return: pass\\n :rtype: bool\\n \\\"\\\"\\\"\\n if np.count_nonzero(ratemap) > max_area_bins:\\n return False\\n\\n if 'min_included_value' in stability_kwargs and stability_kwargs['min_included_value'] <= 0:\\n raise Exception('This module uses 0 values as indication of outside of bin areas.\\\\n'\\n + 'Therefore, min_included_value must be above 0 for stability computation,\\\\n'\\n + 'but is currently {}'.format(stability_kwargs['min_included_value']))\\n stability = compute_field_stability(ratemap, stability_ratemaps[0],\\n stability_ratemaps[1], **stability_kwargs)\\n if np.isnan(stability) or stability < min_stability:\\n return False\\n\\n return True\\n\\n\\ndef create_field_ratemap(ratemap, field_idx):\\n \\\"\\\"\\\"Returns a copy of input `ratemap`, where fields not True in `field_idx` are set to `0`.\\n\\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins)\\n :param numpy.ndarray field_idx: boolean array same shape as ratemap, specifying which elements\\n belong to the field.\\n :return: field_ratemap\\n :rtype: numpy.ndarray\\n \\\"\\\"\\\"\\n field_ratemap = ratemap.copy()\\n field_ratemap[~field_idx] = 0\\n\\n return field_ratemap\\n\\n\\ndef get_field_map(ratemap):\\n \\\"\\\"\\\"Returns the contiguous region map as identified with :py:func:`scipy.ndimage.label`.\\n\\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins)\\n :return: same as input but integer identifiers marking which elements belong to which proximal group\\n :rtype: numpy.ndarray\\n \\\"\\\"\\\"\\n return ndimage.label(ratemap > 0)[0]\\n\\n\\ndef get_filtered_subfield_ratemaps(ratemap, primary_filter_kwargs):\\n \\\"\\\"\\\"Returns a list containing a copy of ratemap for each field that passes the primary filter\\n :py:func:`spatial.fields.primary_filter` where values outside the field are set `0`.\\n\\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins)\\n :param dict primary_filter_kwargs: see :py:func:`spatial.field.primary_filter` keyword arguments.\\n filter_kwargs is passed on to that function as `**filter_kwargs` after `ratemap` argument.\\n :return: field_ratemaps\\n :rtype: list\\n \\\"\\\"\\\"\\n field_map = get_field_map(ratemap)\\n\\n field_nrs = np.unique(field_map)[1:] # ignores the 0 background field\\n\\n # If no fields were detected, return no fields\\n if len(field_nrs) == 0:\\n return []\\n\\n field_ratemaps = []\\n for field_nr in field_nrs:\\n field_ratemap = create_field_ratemap(ratemap, field_map == field_nr)\\n if primary_filter(field_ratemap, **primary_filter_kwargs):\\n field_ratemaps.append(field_ratemap)\\n\\n return field_ratemaps\\n\\n\\ndef detect_field_candidates(ratemap, base_threshold, threshold_step, primary_filter_kwargs):\\n \\\"\\\"\\\"Returns a list of field_candidates that passed primary_filter.\\n\\n The returned field_candidates list is a nested list with the following structure:\\n - Each element in list contains two elements.\\n - The first element is the origin index within field_candidates. This is None for first subfields,\\n but int after.\\n - The second element is a list of ratemaps with increasingly higher threshold, that all\\n have only a single continugous region. The final element in the ratemap list is None,\\n if no further subfields were found with the next threshold (none passed the primary_filter).\\n - If more than one subfield is found, these are separately appended to field_candidates list following\\n the same structure and using the origin index of the previous level where they were detected.\\n\\n - The field_candidates list is populated in an iterative fashion. Each iteration the ratemap threshold is increase\\n by threshold_step.\\n - If a single new field passes primary_filter, it is appended to the list of ratemaps for that level and\\n procedure is repeated.\\n - If no new fields pass primary_filter, the ratemap list is ended with None. This causes the loop to start\\n the procedure on the next level in the field_candidates list.\\n - New levels are added to the field_candidates list each time two or more fields are detected and pass the\\n primary_filter. In that case, after the new fields are appended to field_candidates list,\\n the loop moves on to next level.\\n - As a result the loop extracts all possible field_candidates and also forms links between them.\\n This allows working backwards through this list along the order of inheritance (field and subfield links).\\n\\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) ratemap. Any numpy.nan elements should\\n be replaced with zeros before passing ratemap to this function\\n :param float base_threshold: baseline threshold level from which to start detecting fields\\n :param float threshold_step: threshold shift at each iteration\\n :param dict primary_filter_kwargs: see :py:func:`spatial.field.primary_filter` keyword arguments.\\n filter_kwargs is passed on to that function as `**filter_kwargs` after `ratemap` argument.\\n :return: field_candidates\\n :rtype: list\\n \\\"\\\"\\\"\\n\\n # Use a copy of original input ratemap, threshold and threshold the ratemap.\\n ratemap = ratemap.copy()\\n current_threshold = deepcopy(base_threshold)\\n ratemap[ratemap < current_threshold] = 0\\n\\n # Find all subfields in the ratemap at baseline threshold\\n subfields = get_filtered_subfield_ratemaps(ratemap, primary_filter_kwargs)\\n\\n # If no subfields were found, return an empty list\\n if len(subfields) == 0:\\n return []\\n\\n # Create fields list with the initial subfields\\n field_candidates = []\\n for subfield in subfields:\\n field_candidates.append((None, [subfield]))\\n\\n current_level = 0\\n\\n # Continuoue the loop until the last level ratemap list ends with None\\n while not (field_candidates[-1][1][-1] is None):\\n\\n # If current level ratemaplist ends with None, move to next level\\n if field_candidates[current_level][1][-1] is None:\\n current_level += 1\\n\\n # Increase current_threshold and create a copy of current_ratemap with the current_threshold\\n current_threshold += threshold_step\\n current_ratemap = field_candidates[current_level][1][-1].copy()\\n current_ratemap[current_ratemap < current_threshold] = 0\\n\\n # Find subfields for current level\\n subfields = get_filtered_subfield_ratemaps(current_ratemap, primary_filter_kwargs)\\n\\n if len(subfields) == 0:\\n # If no subfields were found, end the current level ratemap list with None\\n field_candidates[current_level][1].append(None)\\n\\n elif len(subfields) == 1:\\n # If a single field was found, append to current level ratemap list\\n field_candidates[current_level][1].append(subfields[0])\\n\\n else:\\n # If more than one field was found, append these to field_candidates list\\n for subfield in subfields:\\n field_candidates.append((current_level, [subfield]))\\n # If more than one field was found, move to next level\\n current_level += 1\\n\\n return field_candidates\\n\\n\\ndef extract_fields_from_field_candidates(field_candidates, secondary_filter_kwargs):\\n \\\"\\\"\\\"Returns the field_ratemap with lowest threshold of each field_candidate that\\n passes the :py:func:`secondary_filter`.\\n\\n Iterates through field_candidates in reverse order of detection in :py:func:`detect_field_candidates`.\\n Ignores any field_candiate elements that were the origin of sub-fields that pass secondary_filter.\\n\\n :param list field_candidates: output from :py:func:`detect_field_candidates`\\n :param dict secondary_filter_kwargs: see :py:func:`spatial.field.secondary_filter` keyword arguments.\\n filter_kwargs is passed on to that function as `**filter_kwargs` after `ratemap` argument.\\n :return: field_ratemaps\\n :rtype: list\\n \\\"\\\"\\\"\\n field_ratemap_dicts = []\\n\\n # Loop through levels of the field_candidates list starting from the last\\n for current_level in range(len(field_candidates))[::-1]:\\n\\n # Get the field_candidate element for the current_level\\n field_candidate = field_candidates[current_level]\\n\\n # Find out if current level field_candidate has any sub-fields already passed the secondary_filter\\n n_subfields = 0\\n for field_ratemap_dict in field_ratemap_dicts:\\n if field_ratemap_dict['origin'] == current_level:\\n n_subfields += 1\\n\\n # If more than one subfield has been identified for this field_candidate,\\n # pass origin of this field_candidate to detected subfields and skip processing this field_candidate.\\n if n_subfields > 1:\\n for field_ratemap_dict in field_ratemap_dicts:\\n if field_ratemap_dict['origin'] == current_level:\\n field_ratemap_dict['origin'] = field_candidate[0]\\n continue\\n\\n field_ratemap = None\\n\\n # Loop through the ratemaps of this field_candiate\\n for field_candidate_ratemap in field_candidate[1][::-1]:\\n\\n # field_candidate_ratemap lists can end in None. Ignore these elements.\\n if field_candidate_ratemap is None:\\n continue\\n\\n if secondary_filter(field_candidate_ratemap, **secondary_filter_kwargs):\\n # If a ratemap passes the secondary_filter, overwrite the field_ratemap\\n # This way final field_ratemap is the one detected with lowest threshold\\n # but still passes the secondary_filter.\\n field_ratemap = field_candidate_ratemap\\n\\n if field_ratemap is None:\\n\\n if n_subfields == 1:\\n # If no field_ratemap passed the secondary_filter fo this field_candidate,\\n # but this field_candidate had one subfield passing through the filter earlier\\n # assign the origin of that subfield to be the origin of current field_candidate.\\n subfield_index = [field_ratemap_dict['origin']\\n for field_ratemap_dict in field_ratemap_dicts].index(current_level)\\n field_ratemap_dicts[subfield_index]['origin'] = field_candidate[0]\\n\\n else:\\n\\n # If a field_ratemap did pass the secondary_filter, append it to field_ratemap_dicts\\n field_ratemap_dicts.append({'ratemap': field_ratemap,\\n 'origin': field_candidate[0]})\\n\\n if n_subfields == 1:\\n # Remove the single subfield of the current field_candidate from field_ratemap_dicts list\\n subfield_index = [field_ratemap_dict['origin']\\n for field_ratemap_dict in field_ratemap_dicts].index(current_level)\\n del field_ratemap_dicts[subfield_index]\\n\\n field_ratemaps = [field_ratemap_dict['ratemap'] for field_ratemap_dict in field_ratemap_dicts\\n if 'ratemap' in field_ratemap_dict]\\n\\n return field_ratemaps\\n\\n\\ndef detect_fields(ratemap, stability_ratemaps, base_threshold, threshold_step,\\n primary_filter_kwargs, secondary_filter_kwargs):\\n \\\"\\\"\\\"Returns a list of copies of input `ratemap` with every value except those in field\\n replaced by `numpy.nan`.\\n\\n :param numpy.ndarray ratemap: shape (n_ybins, n_xbins) ratemap for fields to be detected.\\n Values to be ignored should be set to numpy.nan.\\n :param tuple stability_ratemaps: tuple with two ratemaps that are used for computing the stability\\n of a field_ratemap after masking them with the non-numpy.nan elements in field_ratemap\\n :param float base_threshold: values below this are set to numpy.nan and ignored\\n :param float threshold_step: the step in ratemap values for iterative detection of fields.\\n :param dict primary_filter_kwargs: see :py:func:`spatial.field.primary_filter` keyword arguments.\\n filter_kwargs is passed on to that function as `**filter_kwargs` after `ratemap` argument.\\n :param dict secondary_filter_kwargs: see :py:func:`spatial.field.secondary_filter` keyword arguments.\\n `'max_relative_bins'` element is replaced with `'max_area_bins'` computed based on the `ratemap`.\\n secondary_filter_kwargs is then passed on to that function as `**filter_kwargs` after `ratemap` argument.\\n :return: field_ratemaps list of ratemaps where values outside field are numpy.nan\\n :rtype: list\\n \\\"\\\"\\\"\\n # Add stability_ratemaps and max_area_bins to a copy of secondary_filter_kwargs\\n secondary_filter_kwargs = deepcopy(secondary_filter_kwargs)\\n secondary_filter_kwargs['stability_ratemaps'] = stability_ratemaps\\n secondary_filter_kwargs['max_area_bins'] = \\\\\\n np.sum(~np.isnan(ratemap)) * secondary_filter_kwargs.pop('max_relative_bins')\\n\\n # Ensure original ratemap is not modified\\n ratemap = ratemap.copy()\\n\\n # Ensure ratemap is in float dtype to support nans that are required in final output format\\n if not isinstance(ratemap.dtype, np.floating):\\n ratemap = ratemap.astype(np.float64)\\n\\n # Set numpy.nan values to 0 for compatibility with field detection methods.\\n ratemap[np.isnan(ratemap)] = 0\\n\\n # Detect field candidates and extract those that pass all filters\\n field_candidates = detect_field_candidates(ratemap, base_threshold, threshold_step, primary_filter_kwargs)\\n field_ratemaps = extract_fields_from_field_candidates(field_candidates, secondary_filter_kwargs)\\n\\n # Set field_ratemap values of 0 to numpy.nan to indicate outside of field areas\\n for i, field_ratemap in enumerate(field_ratemaps):\\n field_ratemaps[i][field_ratemap == 0] = np.nan\\n\\n return field_ratemaps\\n\\n\\ndef get_field_regionprops(field_ratemap, data_x_values=None, data_y_values=None,\\n orientation_x_axis_0_to_pi=True):\\n \\\"\\\"\\\"Returns output from :py:func:`skimage.measure.regionprops` for the boolean field map.\\n\\n Note, if data_x_values and data_y_values (both or neither must be provided), these are used\\n to estimate corresponding centroid_x, centroid_y, minor_axis and major_axis values.\\n\\n Note, data_x_values and data_y_values must have equal and constant spacing in values,\\n as the correction on minor_axis and major_axis length are based on this assumption.\\n\\n :param numpy.ndarray field_ratemap: shape (n_ybins, n_xbins) ratemap with numpy.nan values\\n outside the field.\\n :param numpy.ndarray data_x_values: if provided, to estimate position and axes length values\\n :param numpy.ndarray data_y_values: if provided, to estimate position and axes length values\\n :param bool orientation_x_axis_0_to_pi: if True (default), orientation value is set to range from\\n 0 to pi, increasing clock-wise from parallel to x-axis.\\n :return: dictionary with selection of field properties returned by :py:func:`skimage.measure.regionprops`\\n :rtype: dict\\n \\\"\\\"\\\"\\n if (data_x_values is None) != (data_y_values is None):\\n raise ValueError('data_x_values and data_y_values must both be provided or neither.')\\n\\n if not (data_x_values is None):\\n x_offset = data_x_values[0]\\n y_offset = data_y_values[0]\\n pixel_edge_length = data_x_values[1] - data_x_values[0]\\n else:\\n x_offset = None\\n y_offset = None\\n pixel_edge_length = None\\n\\n out = regionprops(np.uint8(~np.isnan(field_ratemap)), coordinates='rc')[0]\\n return {'area': out.area,\\n 'centroid_x': (out.centroid[1] if (data_x_values is None)\\n else out.centroid[1] * pixel_edge_length + x_offset),\\n 'centroid_y': (out.centroid[0] if (data_y_values is None)\\n else out.centroid[0] * pixel_edge_length + y_offset),\\n 'eccentricity': out.eccentricity,\\n 'minor_axis': (out.minor_axis_length if pixel_edge_length is None\\n else out.minor_axis_length * pixel_edge_length),\\n 'major_axis': (out.major_axis_length if pixel_edge_length is None\\n else out.major_axis_length * pixel_edge_length),\\n 'orientation': ((-out.orientation + np.pi / 2.) if orientation_x_axis_0_to_pi\\n else out.orientation)}\\n\\n\\ndef compute_field_contour(field_ratemap, flipud=True):\\n \\\"\\\"\\\"Returns contour points of the field in the field_ratemap.\\n\\n Assumes input has only one contiguous region of values above 0 and not `numpy.nan`,\\n\\n :param numpy.ndarray field_ratemap: shape (n_ybins, n_xbins) ratemap with numpy.nan values\\n outside the field.\\n :return: contours list of contour points\\n :param bool flipud: if True (default), the field_ratemap array is flipped prior to extracting\\n the contour. This yields correct contour point values relative to field_ratemap array indices.\\n :rtype: list\\n \\\"\\\"\\\"\\n # The ratemap needs to be flipped on the vertical axis, because OpenCV starts counting\\n # y-axis positions from bottom edge, but our analysis assumes position values are counted\\n # ascending from the top edge of the ratemap array.\\n if flipud:\\n field_ratemap = np.flipud(field_ratemap.copy())\\n\\n # Convert input to binary\\n field_ratemap[np.isnan(field_ratemap)] = 0\\n field_binary_map = np.uint8(field_ratemap > 0)\\n\\n # Only keep the first contour from output list, as input should only have one region\\n contour = cv.findContours(field_binary_map, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)[0][0]\\n\\n return contour\\n\\n\\ndef compute_field_ellipse(field_ratemap, flipud=False):\\n \\\"\\\"\\\"\\n\\n :param numpy.ndarray field_ratemap: shape (n_ybins, n_xbins) ratemap with numpy.nan values\\n outside the field.\\n :param bool flipud: if False (default), ratemap is not flipped along first dimension for\\n detecting contour prior to ellipse fitting. This yields correct ellipse coordinates\\n relative to field_ratemap array indices.\\n :return:\\n \\\"\\\"\\\"\\n\\n contour = compute_field_contour(field_ratemap, flipud=flipud)\\n\\n (x, y), (MA, ma), angle = cv.fitEllipse(contour)\\n\\n return {'x': x, 'y': y, 'major_axis_width': MA, 'minor_axis_width': ma, 'angle': angle}\\n\",\n \"\\nimport numpy as np\\n\\nfrom barrylab_ephys_analysis.blea_utils import smooth_signal_with_gaussian\\nfrom barrylab_ephys_analysis.lfp.processing import bandpass_filter, hilbert_fast\\nfrom barrylab_ephys_analysis.base import AnalogSignal\\n\\n\\nclass FrequencyBandAmplitude(AnalogSignal):\\n \\\"\\\"\\\"\\n Computes frequency band amplitude for a recording and provides convenient access based on\\n :py:class:`barrylab_ephys_analysis.base.AnalogSignal`.\\n\\n If existing data with same name is found in recording.analysis['analog_signals'],\\n then that data is used instead, if it matches the input parameters.\\n \\\"\\\"\\\"\\n\\n def __init__(self, *args, highpass_frequency=None, lowpass_frequency=None,\\n filter_order=None, temporal_smoothing_sigma=None, common_average=False,\\n channel_group_tetrodes=None, verbose=False, **kwargs):\\n \\\"\\\"\\\"\\n :param highpass_frequency: Hz (must be provided)\\n :param lowpass_frequency: Hz (must be provided)\\n :param int filter_order: (must be provided)\\n :param float temporal_smoothing_sigma: temporal smoothing gaussian stdev in seconds (must be provided)\\n :param bool common_average: specifies if common average is compute for each channel group before processing.\\n Default is False, no common averaging.\\n :param dict channel_group_tetrodes: {channel_group_A: 5, channel_group_B: [6, 8]} specifies that for\\n channel_group_A, only tetrode 5 is used and for channel_group_B, tetrodes 6 and 8 are used.\\n Default is to use all tetrodes on all channel groups. This must be provided for all groups or none.\\n :param bool verbose: if True, prints progress. Default is False.\\n \\\"\\\"\\\"\\n self._params = {\\n 'highpass_frequency': np.array(highpass_frequency),\\n 'lowpass_frequency': np.array(lowpass_frequency),\\n 'filter_order': np.array(filter_order),\\n 'temporal_smoothing_sigma': np.array(temporal_smoothing_sigma),\\n 'common_average': np.array(common_average),\\n 'channel_group_tetrodes': ({key: np.array(value) for key, value in channel_group_tetrodes.items()}\\n if isinstance(channel_group_tetrodes, dict) else np.array(np.nan))\\n }\\n self.verbose = verbose\\n super(FrequencyBandAmplitude, self).__init__(*args, **kwargs)\\n\\n @staticmethod\\n def compute_frequency_band_amplitude(continuous, sampling_rate, highpass_frequency,\\n lowpass_frequency, filter_order):\\n \\\"\\\"\\\"Returns amplitude envelope of a signal in 1-D array\\n\\n :param numpy.ndarray continuous: shape (N,)\\n :param sampling_rate: Hz\\n :param highpass_frequency: Hz\\n :param lowpass_frequency: Hz\\n :param int filter_order:\\n :return: amplitude_envelope\\n :rtype: numpy.ndarray\\n \\\"\\\"\\\"\\n filtered_signal = bandpass_filter(continuous, sampling_rate=sampling_rate,\\n lowpass_frequency=lowpass_frequency,\\n highpass_frequency=highpass_frequency,\\n filter_order=filter_order)\\n return np.abs(hilbert_fast(filtered_signal))\\n\\n def compute_values_for_continuous_array(self, continuous):\\n \\\"\\\"\\\"Computes frequency band amplitude for along first dimension for all columns.\\n\\n This can be overwritten by a subclass to provide a different value for continuous array,\\n but must return an array of same shape as it receives.\\n\\n Note! The output from this function is averaged along the second dimension, using np.mean(x, axis=1).\\n\\n :param numpy.ndarray continuous: shape (n_samples, n_channels)\\n :return: value_array shape (n_samples, n_channels)\\n :rtype: numpy.ndarray\\n \\\"\\\"\\\"\\n return np.apply_along_axis(\\n FrequencyBandAmplitude.compute_frequency_band_amplitude, 0, continuous,\\n self.recording.continuous['sampling_rate'],\\n highpass_frequency=self._params['highpass_frequency'],\\n lowpass_frequency=self._params['lowpass_frequency'],\\n filter_order=self._params['filter_order']\\n )\\n\\n @staticmethod\\n def smooth_signal(signal, temporal_smoothing_sigma, sampling_rate):\\n return smooth_signal_with_gaussian(signal, temporal_smoothing_sigma * sampling_rate)\\n\\n def compute_for_channel_group(self, channel_group):\\n\\n if isinstance(self._params['channel_group_tetrodes'], dict):\\n tetrode_nrs = self._params['channel_group_tetrodes'][channel_group]\\n else:\\n tetrode_nrs = self.recording.channel_map[channel_group]['tetrode_nrs']\\n\\n if self._params['common_average'] and len(tetrode_nrs) > 1:\\n\\n common_average = np.mean(self.recording.continuous['continuous'][:, tetrode_nrs], axis=1)\\n continuous = np.apply_along_axis(lambda x: x - common_average, 0,\\n self.recording.continuous['continuous'][:, tetrode_nrs])\\n else:\\n\\n continuous = self.recording.continuous['continuous'][:, tetrode_nrs]\\n\\n if len(continuous.shape) == 1:\\n continuous = continuous[:, np.newaxis]\\n\\n amplitude = np.mean(self.compute_values_for_continuous_array(continuous), axis=1)\\n\\n if (not (self._params['temporal_smoothing_sigma'] is None)\\n and self._params['temporal_smoothing_sigma'] != 0):\\n amplitude = self.smooth_signal(amplitude, self._params['temporal_smoothing_sigma'],\\n self.recording.continuous['sampling_rate'])\\n\\n return amplitude\\n\\n def compute(self, recompute=False):\\n\\n # If available data params match input params, skip recomputing, unless requested\\n if not recompute and not (self.data is None):\\n params_match = []\\n for key, item in self._params.items():\\n if key == 'channel_group_tetrodes' and not isinstance(item, dict):\\n continue\\n params_match.append(self.data['params'][key] == item)\\n if all(params_match):\\n if self.verbose:\\n print('Exisiting {} params match input, skipping computation.'.format(self.name))\\n return\\n\\n if self.verbose:\\n print('Computing {}'.format(self.name))\\n\\n signals = []\\n labels = []\\n\\n for channel_group in self.recording.channel_map:\\n signals.append(self.compute_for_channel_group(channel_group)[:, None])\\n labels.append(channel_group)\\n\\n self.data = {'first_timestamp': np.array(self.recording.continuous['timestamps'][0]),\\n 'sampling_rate': np.array(self.recording.continuous['sampling_rate']),\\n 'signal': np.concatenate(signals, axis=1),\\n 'channel_labels': labels,\\n 'params': self._params}\\n\\n\\nclass FrequencyBandFrequency(FrequencyBandAmplitude):\\n \\\"\\\"\\\"\\n Computes frequency band instantaneous frequency for a recording and provides convenient access based on\\n :py:class:`FrequencyBandAmplitude` and :py:class:`barrylab_ephys_analysis.base.AnalogSignal`.\\n \\\"\\\"\\\"\\n\\n @staticmethod\\n def compute_frequency_band_frequency(continuous, sampling_rate, highpass_frequency,\\n lowpass_frequency, filter_order):\\n \\\"\\\"\\\"Returns instantaneous frequency of a signal in 1-D array.\\n\\n Note! Due to the nature of computing instantaneous frequency, the values are\\n shifted forward by half a sample.\\n\\n :param numpy.ndarray continuous: shape (N,)\\n :param sampling_rate: Hz\\n :param highpass_frequency: Hz\\n :param lowpass_frequency: Hz\\n :param int filter_order:\\n :return: instantaneous_frequency shape (N,)\\n :rtype: numpy.ndarray\\n \\\"\\\"\\\"\\n filtered_signal = bandpass_filter(continuous, sampling_rate=sampling_rate,\\n lowpass_frequency=lowpass_frequency,\\n highpass_frequency=highpass_frequency,\\n filter_order=filter_order)\\n instantaneous_phase = np.unwrap(np.angle(hilbert_fast(filtered_signal)))\\n instantaneous_frequency = (np.diff(instantaneous_phase) / (2.0 * np.pi) * sampling_rate)\\n instantaneous_frequency = np.concatenate((instantaneous_frequency[:1], instantaneous_frequency))\\n return instantaneous_frequency\\n\\n def compute_values_for_continuous_array(self, continuous):\\n \\\"\\\"\\\"Computes instantaneous frequency along first dimension for all columns.\\n\\n :param numpy.ndarray continuous: shape (n_samples, n_channels)\\n :return: value_array shape (n_samples, n_channels)\\n :rtype: numpy.ndarray\\n \\\"\\\"\\\"\\n return np.apply_along_axis(\\n FrequencyBandFrequency.compute_frequency_band_frequency, 0, continuous,\\n self.recording.continuous['sampling_rate'],\\n highpass_frequency=self._params['highpass_frequency'],\\n lowpass_frequency=self._params['lowpass_frequency'],\\n filter_order=self._params['filter_order']\\n )\\n\\n\\nclass FrequencyBandPhase(FrequencyBandAmplitude):\\n \\\"\\\"\\\"\\n Computes frequency band instantaneous phase for a recording and provides convenient access based on\\n :py:class:`FrequencyBandAmplitude` and :py:class:`barrylab_ephys_analysis.base.AnalogSignal`.\\n\\n Phase is computed and stored in radians in unwrapped form.\\n\\n .. warning::\\n\\n If temporal_smoothing_sigma argument above 0 is provided, then phase is unwrapped using\\n :py:func:`numpy.unwrap` and wrapped again using :py:func:`FrequencyBandPhase.wrap_phase_to_range_between_pi`.\\n It is important that the sampling rate of the signal is sufficiently high for this method to work.\\n\\n \\\"\\\"\\\"\\n\\n @staticmethod\\n def wrap_phase_to_range_between_pi(signal):\\n \\\"\\\"\\\"Returns input signal wrapped to range between -pi to pi\\n\\n :param numpy.ndarray signal:\\n :return: wrapped_signal\\n \\\"\\\"\\\"\\n return np.mod(signal + np.pi, 2 * np.pi) - np.pi\\n\\n @staticmethod\\n def compute_frequency_band_phase(continuous, sampling_rate, highpass_frequency,\\n lowpass_frequency, filter_order):\\n \\\"\\\"\\\"Returns instantaneous phase of a signal in 1-D array in unwrapped form.\\n\\n :param numpy.ndarray continuous: shape (N,)\\n :param sampling_rate: Hz\\n :param highpass_frequency: Hz\\n :param lowpass_frequency: Hz\\n :param int filter_order:\\n :return: instantaneous_phase shape (N,)\\n :rtype: numpy.ndarray\\n \\\"\\\"\\\"\\n filtered_signal = bandpass_filter(continuous, sampling_rate=sampling_rate,\\n lowpass_frequency=lowpass_frequency,\\n highpass_frequency=highpass_frequency,\\n filter_order=filter_order)\\n instantaneous_phase = np.angle(hilbert_fast(filtered_signal))\\n return instantaneous_phase\\n\\n def compute_values_for_continuous_array(self, continuous):\\n \\\"\\\"\\\"Computes instantaneous phase along first dimension for all columns.\\n\\n :param numpy.ndarray continuous: shape (n_samples, n_channels)\\n :return: value_array shape (n_samples, n_channels)\\n :rtype: numpy.ndarray\\n \\\"\\\"\\\"\\n return np.apply_along_axis(\\n FrequencyBandPhase.compute_frequency_band_phase, 0, continuous,\\n self.recording.continuous['sampling_rate'],\\n highpass_frequency=self._params['highpass_frequency'],\\n lowpass_frequency=self._params['lowpass_frequency'],\\n filter_order=self._params['filter_order']\\n )\\n\\n @staticmethod\\n def smooth_signal(signal, temporal_smoothing_sigma, sampling_rate):\\n return FrequencyBandPhase.wrap_phase_to_range_between_pi(\\n smooth_signal_with_gaussian(np.unwrap(signal, axis=0), temporal_smoothing_sigma * sampling_rate)\\n )\\n\\n\\nclass ThetaDeltaRatio(AnalogSignal):\\n\\n def __init__(self, *args, theta_band_amplitude=None, delta_band_amplitude=None,\\n speed=None, mean_ratio_min_speed=None, **kwargs):\\n \\\"\\\"\\\"\\n :param theta_band_amplitude: :py:class:`FrequencyBandAmplitude` instantiated for theta frequency band\\n :param delta_band_amplitude: :py:class:`FrequencyBandAmplitude` instantiated for delta frequency band\\n :param speed: :py:class:`barrylab_ephys_analysis.base.AnalogSignal` for animal speed\\n :param float mean_ratio_min_speed: minimum animal speed threshold for calculating mean theta/delta ratio\\n \\\"\\\"\\\"\\n self._theta_band_amplitude = theta_band_amplitude\\n self._delta_band_amplitude = delta_band_amplitude\\n self._speed = speed\\n self._params = {'mean_ratio_min_speed': np.array(mean_ratio_min_speed)}\\n super(ThetaDeltaRatio, self).__init__(*args, **kwargs)\\n\\n def compute(self, recompute=False):\\n\\n # If available data params match input params, skip recomputing, unless requested\\n if not recompute and not (self.data is None):\\n if all([self.data['params'][key] == item for key, item in self._params.items()]):\\n return\\n\\n print('Computing ThetaDeltaRatio')\\n\\n # Compute mean np.log(ratio) of theta/delta amplitude during running epochs\\n running_epoch_timestamps = self._speed.get_epoch_timestamps_function_of_signal_true(\\n lambda x: np.array(x > self._params['mean_ratio_min_speed']).squeeze()\\n )\\n running_theta_amplitude = np.mean(\\n np.concatenate(\\n [self._theta_band_amplitude.get_values_between_timestamps(*ets, allow_clipping=True)[0]\\n for ets in running_epoch_timestamps]\\n ),\\n axis=1\\n )\\n running_delta_amplitude = np.mean(\\n np.concatenate(\\n [self._delta_band_amplitude.get_values_between_timestamps(*ets, allow_clipping=True)[0]\\n for ets in running_epoch_timestamps]\\n ),\\n axis=1\\n )\\n running_mean_ratio = np.mean(np.log(running_theta_amplitude / running_delta_amplitude))\\n\\n # Get theta/delta ratio with timestamps\\n ratio = np.log(np.mean(self._theta_band_amplitude.data['signal'], axis=1)\\n / np.mean(self._delta_band_amplitude.data['signal'], axis=1))\\n\\n self.data = {'first_timestamp': self._theta_band_amplitude.data['first_timestamp'],\\n 'sampling_rate': self._theta_band_amplitude.data['sampling_rate'],\\n 'signal': ratio[:, None],\\n 'channel_labels': [''],\\n 'params': self._params,\\n 'running_mean_ratio': np.array(running_mean_ratio)}\\n\"\n]","isConversational":false},"apis":{"kind":"list like","value":[["matplotlib.pyplot.imshow","matplotlib.pyplot.title","matplotlib.pyplot.clim","scipy.stats.pearsonr","numpy.vstack","matplotlib.pyplot.get_cmap","numpy.degrees","numpy.append","matplotlib.pyplot.subplot","numpy.std","numpy.diff","numpy.mean","matplotlib.pyplot.axis","numpy.load","pandas.read_pickle","matplotlib.pyplot.show"],["numpy.nanmax","numpy.unique","numpy.isnan","numpy.uint8","scipy.ndimage.label","numpy.count_nonzero"],["numpy.log","numpy.concatenate","numpy.unwrap","numpy.apply_along_axis","numpy.mean","numpy.diff","numpy.mod","numpy.array"]],"string":"[\n [\n \"matplotlib.pyplot.imshow\",\n \"matplotlib.pyplot.title\",\n \"matplotlib.pyplot.clim\",\n \"scipy.stats.pearsonr\",\n \"numpy.vstack\",\n \"matplotlib.pyplot.get_cmap\",\n \"numpy.degrees\",\n \"numpy.append\",\n \"matplotlib.pyplot.subplot\",\n \"numpy.std\",\n \"numpy.diff\",\n \"numpy.mean\",\n \"matplotlib.pyplot.axis\",\n \"numpy.load\",\n \"pandas.read_pickle\",\n \"matplotlib.pyplot.show\"\n ],\n [\n \"numpy.nanmax\",\n \"numpy.unique\",\n \"numpy.isnan\",\n \"numpy.uint8\",\n \"scipy.ndimage.label\",\n \"numpy.count_nonzero\"\n ],\n [\n \"numpy.log\",\n \"numpy.concatenate\",\n \"numpy.unwrap\",\n \"numpy.apply_along_axis\",\n \"numpy.mean\",\n \"numpy.diff\",\n \"numpy.mod\",\n \"numpy.array\"\n ]\n]","isConversational":false}}},{"rowIdx":1299,"cells":{"repo_name":{"kind":"string","value":"alik-git/mbrl-lib"},"hexsha":{"kind":"list like","value":["b364f8e64ca71ebd18147fe8cdbd3068b74e1f1e","b364f8e64ca71ebd18147fe8cdbd3068b74e1f1e"],"string":"[\n \"b364f8e64ca71ebd18147fe8cdbd3068b74e1f1e\",\n \"b364f8e64ca71ebd18147fe8cdbd3068b74e1f1e\"\n]","isConversational":false},"file_path":{"kind":"list like","value":["mbrl/examples/main.py","mbrl/models/planet_legacy.py"],"string":"[\n \"mbrl/examples/main.py\",\n \"mbrl/models/planet_legacy.py\"\n]","isConversational":false},"code":{"kind":"list like","value":["# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\nfrom typing import Iterable\nimport hydra\nimport numpy as np\nimport omegaconf\nimport torch\n\nimport mbrl.algorithms.mbpo as mbpo\nimport mbrl.algorithms.pets as pets\nimport mbrl.algorithms.planet as planet\nimport mbrl.algorithms.dreamer as dreamer #added April 2022 for project\nimport mbrl.util.env\n\nimport pandas as pd\nfrom collections import Iterable\n\nimport wandb\n\ndef flatten_config(cfg, curr_nested_key):\n \"\"\"The nested config file provided by Hydra cannot be parsed by wandb. This recursive function flattens the config file, separating the nested keys and their parents via an underscore. Allows for easier configuration using wandb.\n\n Args:\n cfg (Hydra config): The nested config file used by Hydra.\n curr_nested_key (str): The current parent key (used for recursive calls).\n\n Returns:\n (dict): A flatt configuration dictionary.\n \"\"\" \n \n flat_cfg = {}\n\n for curr_key in cfg.keys():\n\n # deal with missing values\n try:\n curr_item = cfg[curr_key]\n except Exception as e:\n curr_item = 'NA'\n\n # deal with lists\n if type(curr_item) == list or type(curr_item) == omegaconf.listconfig.ListConfig:\n for nested_idx, nested_item in enumerate(curr_item):\n list_nested_key = f\"{curr_nested_key}_{curr_key}_{nested_idx}\"\n flat_cfg[list_nested_key] = nested_item\n \n # check if item is also a config\n # recurse\n elif isinstance(curr_item, Iterable) and type(curr_item) != str:\n flat_cfg.update(flatten_config(curr_item, f\"{curr_nested_key}_{curr_key}\"))\n\n # otherwise just add to return dict\n else:\n flat_cfg[f\"{curr_nested_key}_{curr_key}\"] = curr_item\n\n return flat_cfg\n\n@hydra.main(config_path=\"conf\", config_name=\"main\")\ndef run(cfg: omegaconf.DictConfig):\n env, term_fn, reward_fn = mbrl.util.env.EnvHandler.make_env(cfg)\n \n for config_item in cfg:\n wandb.config[config_item] = cfg[config_item]\n \n flat_cfg = flatten_config(cfg, \"\")\n for config_item in flat_cfg:\n wandb.config[config_item] = flat_cfg[config_item]\n \n np.random.seed(cfg.seed)\n torch.manual_seed(cfg.seed)\n if cfg.algorithm.name == \"pets\":\n return pets.train(env, term_fn, reward_fn, cfg)\n if cfg.algorithm.name == \"mbpo\":\n test_env, *_ = mbrl.util.env.EnvHandler.make_env(cfg)\n return mbpo.train(env, test_env, term_fn, cfg)\n if cfg.algorithm.name == \"planet\":\n return planet.train(env, cfg)\n if cfg.algorithm.name == \"dreamer\": #added for project\n return dreamer.train(env, cfg)\n\n\nif __name__ == \"__main__\":\n wandb.init(project=\"MBRL_Duckyt\", entity=\"mbrl_ducky\", monitor_gym=True)\n run()\n","import torch\nimport numpy as np\nfrom typing import Tuple, Any, Union, Optional, Dict\nTensorType = Any\n\nclass DMControlSuiteEnv:\n\n def __init__(self, \n name: str, \n max_episode_length: int = 1000,\n action_repeat:int = 2,\n size: Tuple[int] = (64, 64),\n camera: Optional[Any] = None,\n ):\n domain, task = name.split('_', 1)\n if domain == 'cup':\n domain = 'ball_in_cup'\n if isinstance(domain, str):\n from dm_control import suite\n self._env = suite.load(domain, task)\n else:\n assert task is None\n self._env = domain()\n self._size = size\n if camera is None:\n camera = dict(quadruped=2).get(domain, 0)\n self._camera = camera\n self._step = 0\n self._max_episode_length = max_episode_length\n self._action_repeat = action_repeat\n \n @property\n def observation_space(self):\n spaces = {}\n for key, value in self._env.observation_spec().items():\n spaces[key] = gym.spaces.Box(\n -np.inf, np.inf, value.shape, dtype=np.float32)\n spaces['image'] = gym.spaces.Box(\n 0, 255, self._size + (3,), dtype=np.uint8)\n return gym.spaces.Dict(spaces)\n\n @property\n def action_space(self):\n spec = self._env.action_spec()\n return gym.spaces.Box(spec.minimum, spec.maximum, dtype=np.float32)\n\n def step(self, action):\n reward = 0\n obs = None\n for k in range(self._action_repeat):\n time_step = self._env.step(action)\n self._step += 1\n obs = dict(time_step.observation)\n obs['image'] = self.render()\n reward += time_step.reward or 0\n done = time_step.last() or self._step == self._max_episode_length\n if done:\n break\n info = {'discount': np.array(time_step.discount, np.float32)}\n return obs[\"image\"], reward, done, info\n\n def reset(self):\n time_step = self._env.reset()\n self._step = 0\n obs = dict(time_step.observation)\n obs['image'] = self.render()\n return obs[\"image\"]\n\n def render(self, *args, **kwargs):\n if kwargs.get('mode', 'rgb_array') != 'rgb_array':\n raise ValueError(\"Only render mode 'rgb_array' is supported.\")\n return self._env.physics.render(*self._size, camera_id=self._camera)\n\nclass Episode(object):\n \"\"\" Episode Class which contains the related\n attributes of an environment episode in the\n the format similar to queue\"\"\"\n \n def __init__(self,\n obs:TensorType,\n action_space: int = 1,\n action_repeat: int = 2) -> None:\n \"\"\"Initializes a list of all episode attributes\"\"\"\n self.action_space = action_space\n self.action_repeat = action_repeat\n obs = torch.FloatTensor(np.ascontiguousarray(obs.transpose\n ((2, 0, 1))))\n self.t = 1\n self.obs = [obs]\n self.action = [torch.FloatTensor(torch.zeros(1, self.action_space))]\n self.reward = [0]\n self.done = [False]\n \n def append(self, \n episode_attrs: Tuple[TensorType]) -> None:\n \"\"\" Appends episode attribute to the list.\"\"\"\n obs, action, reward, done = episode_attrs\n obs = torch.FloatTensor(np.ascontiguousarray(obs.transpose\n ((2, 0, 1))))\n self.t += 1\n self.obs.append(obs)\n self.action.append(action)\n self.reward.append(reward)\n self.done.append(done)\n \n def reset(self, \n obs:TensorType) -> None:\n \"\"\" Resets Episode list of attributes.\"\"\"\n obs = torch.FloatTensor(np.ascontiguousarray(obs.transpose\n ((2, 0, 1))))\n self.t = 1\n self.obs = [obs]\n self.action = [torch.FloatTensor(torch.zeros(1, self.action_space))]\n self.reward = [0]\n self.done = [False]\n \n def todict(self,) -> Dict:\n episode_dict = dict({'count': self.t,\n 'obs': torch.stack(self.obs),\n 'action': torch.cat(self.action),\n 'reward': torch.FloatTensor(self.reward),\n 'done': torch.BoolTensor(self.done)})\n return "],"string":"[\n \"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\\n#\\n# This source code is licensed under the MIT license found in the\\n# LICENSE file in the root directory of this source tree.\\nfrom typing import Iterable\\nimport hydra\\nimport numpy as np\\nimport omegaconf\\nimport torch\\n\\nimport mbrl.algorithms.mbpo as mbpo\\nimport mbrl.algorithms.pets as pets\\nimport mbrl.algorithms.planet as planet\\nimport mbrl.algorithms.dreamer as dreamer #added April 2022 for project\\nimport mbrl.util.env\\n\\nimport pandas as pd\\nfrom collections import Iterable\\n\\nimport wandb\\n\\ndef flatten_config(cfg, curr_nested_key):\\n \\\"\\\"\\\"The nested config file provided by Hydra cannot be parsed by wandb. This recursive function flattens the config file, separating the nested keys and their parents via an underscore. Allows for easier configuration using wandb.\\n\\n Args:\\n cfg (Hydra config): The nested config file used by Hydra.\\n curr_nested_key (str): The current parent key (used for recursive calls).\\n\\n Returns:\\n (dict): A flatt configuration dictionary.\\n \\\"\\\"\\\" \\n \\n flat_cfg = {}\\n\\n for curr_key in cfg.keys():\\n\\n # deal with missing values\\n try:\\n curr_item = cfg[curr_key]\\n except Exception as e:\\n curr_item = 'NA'\\n\\n # deal with lists\\n if type(curr_item) == list or type(curr_item) == omegaconf.listconfig.ListConfig:\\n for nested_idx, nested_item in enumerate(curr_item):\\n list_nested_key = f\\\"{curr_nested_key}_{curr_key}_{nested_idx}\\\"\\n flat_cfg[list_nested_key] = nested_item\\n \\n # check if item is also a config\\n # recurse\\n elif isinstance(curr_item, Iterable) and type(curr_item) != str:\\n flat_cfg.update(flatten_config(curr_item, f\\\"{curr_nested_key}_{curr_key}\\\"))\\n\\n # otherwise just add to return dict\\n else:\\n flat_cfg[f\\\"{curr_nested_key}_{curr_key}\\\"] = curr_item\\n\\n return flat_cfg\\n\\n@hydra.main(config_path=\\\"conf\\\", config_name=\\\"main\\\")\\ndef run(cfg: omegaconf.DictConfig):\\n env, term_fn, reward_fn = mbrl.util.env.EnvHandler.make_env(cfg)\\n \\n for config_item in cfg:\\n wandb.config[config_item] = cfg[config_item]\\n \\n flat_cfg = flatten_config(cfg, \\\"\\\")\\n for config_item in flat_cfg:\\n wandb.config[config_item] = flat_cfg[config_item]\\n \\n np.random.seed(cfg.seed)\\n torch.manual_seed(cfg.seed)\\n if cfg.algorithm.name == \\\"pets\\\":\\n return pets.train(env, term_fn, reward_fn, cfg)\\n if cfg.algorithm.name == \\\"mbpo\\\":\\n test_env, *_ = mbrl.util.env.EnvHandler.make_env(cfg)\\n return mbpo.train(env, test_env, term_fn, cfg)\\n if cfg.algorithm.name == \\\"planet\\\":\\n return planet.train(env, cfg)\\n if cfg.algorithm.name == \\\"dreamer\\\": #added for project\\n return dreamer.train(env, cfg)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n wandb.init(project=\\\"MBRL_Duckyt\\\", entity=\\\"mbrl_ducky\\\", monitor_gym=True)\\n run()\\n\",\n \"import torch\\nimport numpy as np\\nfrom typing import Tuple, Any, Union, Optional, Dict\\nTensorType = Any\\n\\nclass DMControlSuiteEnv:\\n\\n def __init__(self, \\n name: str, \\n max_episode_length: int = 1000,\\n action_repeat:int = 2,\\n size: Tuple[int] = (64, 64),\\n camera: Optional[Any] = None,\\n ):\\n domain, task = name.split('_', 1)\\n if domain == 'cup':\\n domain = 'ball_in_cup'\\n if isinstance(domain, str):\\n from dm_control import suite\\n self._env = suite.load(domain, task)\\n else:\\n assert task is None\\n self._env = domain()\\n self._size = size\\n if camera is None:\\n camera = dict(quadruped=2).get(domain, 0)\\n self._camera = camera\\n self._step = 0\\n self._max_episode_length = max_episode_length\\n self._action_repeat = action_repeat\\n \\n @property\\n def observation_space(self):\\n spaces = {}\\n for key, value in self._env.observation_spec().items():\\n spaces[key] = gym.spaces.Box(\\n -np.inf, np.inf, value.shape, dtype=np.float32)\\n spaces['image'] = gym.spaces.Box(\\n 0, 255, self._size + (3,), dtype=np.uint8)\\n return gym.spaces.Dict(spaces)\\n\\n @property\\n def action_space(self):\\n spec = self._env.action_spec()\\n return gym.spaces.Box(spec.minimum, spec.maximum, dtype=np.float32)\\n\\n def step(self, action):\\n reward = 0\\n obs = None\\n for k in range(self._action_repeat):\\n time_step = self._env.step(action)\\n self._step += 1\\n obs = dict(time_step.observation)\\n obs['image'] = self.render()\\n reward += time_step.reward or 0\\n done = time_step.last() or self._step == self._max_episode_length\\n if done:\\n break\\n info = {'discount': np.array(time_step.discount, np.float32)}\\n return obs[\\\"image\\\"], reward, done, info\\n\\n def reset(self):\\n time_step = self._env.reset()\\n self._step = 0\\n obs = dict(time_step.observation)\\n obs['image'] = self.render()\\n return obs[\\\"image\\\"]\\n\\n def render(self, *args, **kwargs):\\n if kwargs.get('mode', 'rgb_array') != 'rgb_array':\\n raise ValueError(\\\"Only render mode 'rgb_array' is supported.\\\")\\n return self._env.physics.render(*self._size, camera_id=self._camera)\\n\\nclass Episode(object):\\n \\\"\\\"\\\" Episode Class which contains the related\\n attributes of an environment episode in the\\n the format similar to queue\\\"\\\"\\\"\\n \\n def __init__(self,\\n obs:TensorType,\\n action_space: int = 1,\\n action_repeat: int = 2) -> None:\\n \\\"\\\"\\\"Initializes a list of all episode attributes\\\"\\\"\\\"\\n self.action_space = action_space\\n self.action_repeat = action_repeat\\n obs = torch.FloatTensor(np.ascontiguousarray(obs.transpose\\n ((2, 0, 1))))\\n self.t = 1\\n self.obs = [obs]\\n self.action = [torch.FloatTensor(torch.zeros(1, self.action_space))]\\n self.reward = [0]\\n self.done = [False]\\n \\n def append(self, \\n episode_attrs: Tuple[TensorType]) -> None:\\n \\\"\\\"\\\" Appends episode attribute to the list.\\\"\\\"\\\"\\n obs, action, reward, done = episode_attrs\\n obs = torch.FloatTensor(np.ascontiguousarray(obs.transpose\\n ((2, 0, 1))))\\n self.t += 1\\n self.obs.append(obs)\\n self.action.append(action)\\n self.reward.append(reward)\\n self.done.append(done)\\n \\n def reset(self, \\n obs:TensorType) -> None:\\n \\\"\\\"\\\" Resets Episode list of attributes.\\\"\\\"\\\"\\n obs = torch.FloatTensor(np.ascontiguousarray(obs.transpose\\n ((2, 0, 1))))\\n self.t = 1\\n self.obs = [obs]\\n self.action = [torch.FloatTensor(torch.zeros(1, self.action_space))]\\n self.reward = [0]\\n self.done = [False]\\n \\n def todict(self,) -> Dict:\\n episode_dict = dict({'count': self.t,\\n 'obs': torch.stack(self.obs),\\n 'action': torch.cat(self.action),\\n 'reward': torch.FloatTensor(self.reward),\\n 'done': torch.BoolTensor(self.done)})\\n return \"\n]","isConversational":false},"apis":{"kind":"list like","value":[["torch.manual_seed","numpy.random.seed"],["torch.BoolTensor","torch.cat","torch.zeros","torch.FloatTensor","torch.stack","numpy.array"]],"string":"[\n [\n \"torch.manual_seed\",\n \"numpy.random.seed\"\n ],\n [\n \"torch.BoolTensor\",\n \"torch.cat\",\n \"torch.zeros\",\n \"torch.FloatTensor\",\n \"torch.stack\",\n \"numpy.array\"\n ]\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":12,"numItemsPerPage":100,"numTotalItems":73810,"offset":1200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDIwMTAwOCwic3ViIjoiL2RhdGFzZXRzL3ZlcnNpb24tY29udHJvbC90aGUtc3RhY2stZHMtbGliLTEwMGsiLCJleHAiOjE3NzQyMDQ2MDgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.DLvmnK4NfVQmJRhNZ8UMXfIqamWa9zfDqohjLEE--_D8Lw2jD6uDKiwQEvEd8ykAJA30qbn8pG3WU4yB7_HvAg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":73810,"numBytesParquet":432332402}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
ZJUGuoShuai/vision | [
"a9940fe4b2b63bd82a2f853616e00fd0bd112f9a",
"a9940fe4b2b63bd82a2f853616e00fd0bd112f9a",
"a9940fe4b2b63bd82a2f853616e00fd0bd112f9a"
] | [
"torchvision/models/quantization/resnet.py",
"test/common_utils.py",
"torchvision/__init__.py"
] | [
"from typing import Any, Type, Union, List\n\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\nfrom torch.quantization import fuse_modules\nfrom torchvision.models.resnet import Bottleneck, BasicBlock, ResNet, model_urls\n\nfrom ..._internally_replaced_utils import load_state_dict_from_url\nfrom .util... | [
[
"torch.quantization.fuse_modules",
"torch.quantization.DeQuantStub",
"torch.nn.ReLU",
"torch.nn.quantized.FloatFunctional",
"torch.quantization.QuantStub"
],
[
"torch.set_rng_state",
"torch.jit.script",
"torch.abs",
"torch.randint",
"torch.manual_seed",
"torch.get_r... |
jonathanyepez/Master-C3 | [
"37a3a13ef2bc8f51d3da3ca1b3704569ef83ef3d"
] | [
"CasoPractico01.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jul 6 18:03:09 2020\r\n\r\n@author: Jonathan A. Yepez M.\r\n\"\"\"\r\n\r\n#Task Description\r\n\"\"\"\r\nEn el archivo auto.csv se encuentran los siguientes datos de diferentes automoviles:\r\n * Cilindros\r\n * Cilindrada\r\n * Potencia\r\n * Peso\r... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.mean_squared_error",
"matplotlib.pyplot.setp",
"sklearn.linear_model.LinearRegression",
"pandas.plotting.scatter_matrix",
"matplotlib.pyplot.figure"
]
] |
meltmedia/the-ark | [
"d559897494e02a2e2048fdc44014f17af89691bb"
] | [
"the_ark/screen_capture.py"
] | [
"import math\nimport numpy\nfrom PIL import Image\nfrom the_ark.selenium_helpers import SeleniumHelperExceptions, ElementNotVisibleError, ElementError\nfrom StringIO import StringIO\nimport time\nimport traceback\n\nDEFAULT_SCROLL_PADDING = 100\nSCREENSHOT_FILE_EXTENSION = \"png\"\nDEFAULT_PIXEL_MATCH_OFFSET = 100\... | [
[
"numpy.asarray",
"numpy.array_equal"
]
] |
feng-y16/Hamiltonian-Generative-Networks | [
"702d3ff3aec40eba20e17c5a1612b5b0b1e2f831",
"702d3ff3aec40eba20e17c5a1612b5b0b1e2f831"
] | [
"train.py",
"environments/gravity.py"
] | [
"\"\"\"Script to train the Hamiltonian Generative Network\n\"\"\"\nimport ast\nimport argparse\nimport copy\nimport pprint\nimport os\nimport warnings\nimport yaml\n\nimport numpy as np\nimport torch\nimport tqdm\n\nfrom utilities.integrator import Integrator\nfrom utilities.training_logger import TrainingLogger\nf... | [
[
"torch.optim.Adam",
"torch.__getattribute__",
"numpy.concatenate",
"torch.cuda.is_available",
"torch.clamp"
],
[
"numpy.power",
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin",
"numpy.max",
"numpy.zeros_like",
"numpy.random.randn",
"numpy.random.rand",
"nu... |
puririshi98/benchmark | [
"2440b3b6e177a9b38011eff3ec25f3b6052acfd0",
"79f554f1e1cf36f62994c78e0e6e5b360f554022",
"79f554f1e1cf36f62994c78e0e6e5b360f554022",
"df51ed9c1d6dbde1deef63f2a037a369f8554406",
"79f554f1e1cf36f62994c78e0e6e5b360f554022",
"79f554f1e1cf36f62994c78e0e6e5b360f554022",
"2440b3b6e177a9b38011eff3ec25f3b6052acfd... | [
"DeepLearningExamples/PyTorch/Recommendation/DLRM/preproc/parquet_to_binary.py",
"DeepLearningExamples/TensorFlow2/Segmentation/MaskRCNN/mrcnn_tf2/object_detection/argmax_matcher.py",
"DeepLearningExamples/TensorFlow2/LanguageModeling/BERT/official/nlp/modeling/networks/encoder_scaffold.py",
"DeepLearningExam... | [
"# Copyright (c) 2021 NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"pandas.read_parquet"
],
[
"tensorflow.reduce_max",
"tensorflow.multiply",
"tensorflow.greater",
"tensorflow.shape",
"tensorflow.cast",
"tensorflow.ones",
"tensorflow.one_hot",
"tensorflow.where",
"tensorflow.argmax",
"tensorflow.greater_equal"
],
[
"tensorf... |
jklymak/dolfyn | [
"eea98fe0021886cf654e25293c385c5c3707ff8d",
"eea98fe0021886cf654e25293c385c5c3707ff8d"
] | [
"dolfyn/io/rdi.py",
"docs/examples/adv_motion_correction2.py"
] | [
"import numpy as np\nimport xarray as xr\nfrom .. import time as tmlib\nimport warnings\nfrom os.path import getsize\nfrom ._read_bin import bin_reader\nfrom .base import _find_userdata, _create_dataset, _abspath\nfrom ..rotate.rdi import _calc_beam_orientmat, _calc_orientmat\nfrom ..rotate.base import _set_coords\... | [
[
"numpy.unique",
"numpy.isnan",
"numpy.arange",
"numpy.median",
"numpy.nanmean",
"numpy.array",
"numpy.zeros",
"numpy.empty"
],
[
"matplotlib.pyplot.legend",
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.title",
"numpy.logspace",
"matplotlib.pyplot.plo... |
Prepaire/MolGNN_fewshot | [
"c7c17afdeae7f2ef0c8e3ca2da033091ec7537ca",
"c7c17afdeae7f2ef0c8e3ca2da033091ec7537ca"
] | [
"chem/util.py",
"chem/pretrain_fingerprint.py"
] | [
"import torch\nimport random\nimport collections\n\nimport networkx as nx\nfrom rdkit.Chem import AllChem\nimport numpy as np\n\nfrom loader import graph_data_obj_to_nx_simple, nx_to_graph_data_obj_simple\n\nfrom loader import MoleculeDataset\n\n\ndef get_filtered_fingerprint(smiles):\n \"\"\" Get filtered PubCh... | [
[
"torch.randint",
"torch.empty",
"torch.Tensor",
"torch.cat",
"torch.tensor",
"numpy.delete",
"numpy.vstack"
],
[
"sklearn.metrics.roc_auc_score",
"numpy.random.seed",
"torch.cat",
"torch.manual_seed",
"torch.nn.BCEWithLogitsLoss",
"torch.no_grad",
"torch... |
eunice-chan/train-procgen | [
"3f7cc3e54c535ed41aa9cb510f408e87d74c87aa"
] | [
"train_procgen/evaluate.py"
] | [
"import tensorflow as tf\nfrom baselines.ppo2 import ppo2\nfrom baselines.common.models import build_impala_cnn\nfrom baselines.common.mpi_util import setup_mpi_gpus\nfrom procgen import ProcgenEnv\nfrom baselines.common.vec_env import (\n VecExtractDictObs,\n VecMonitor,\n VecFrameStack,\n VecNormalize... | [
[
"tensorflow.ConfigProto",
"tensorflow.Session"
]
] |
thomasjpfan/pyamg | [
"b0904d31c8da0c29affcd7d0fcd2bb8cb910b42a",
"cabbb008fa26d4c9d8c24decf06374185864c88b"
] | [
"pyamg/aggregation/aggregation.py",
"pyamg/vis/vtk_writer.py"
] | [
"\"\"\"Support for aggregation-based AMG.\"\"\"\nfrom __future__ import absolute_import\n\n\nfrom warnings import warn\nimport numpy as np\nfrom scipy.sparse import csr_matrix, isspmatrix_csr, isspmatrix_bsr,\\\n SparseEfficiencyWarning\n\nfrom pyamg.multilevel import multilevel_solver\nfrom pyamg.relaxation.smo... | [
[
"numpy.asarray",
"scipy.sparse.csr_matrix",
"scipy.sparse.isspmatrix_bsr",
"scipy.sparse.isspmatrix_csr",
"numpy.zeros"
],
[
"numpy.hstack",
"numpy.arange",
"numpy.ones",
"numpy.ndim",
"numpy.zeros",
"numpy.vstack"
]
] |
MuellerSeb/jams_python | [
"1bca04557da79d8f8a4c447f5ccc517c40ab7dfc",
"1bca04557da79d8f8a4c447f5ccc517c40ab7dfc",
"1bca04557da79d8f8a4c447f5ccc517c40ab7dfc",
"1bca04557da79d8f8a4c447f5ccc517c40ab7dfc",
"1bca04557da79d8f8a4c447f5ccc517c40ab7dfc",
"1bca04557da79d8f8a4c447f5ccc517c40ab7dfc"
] | [
"jams/errormeasures.py",
"jams/str2tex.py",
"jams/eddybox/energyclosure.py",
"jams/eddybox/sltclean.py",
"jams/ascii2ascii.py",
"jams/eddybox/profile2storage.py"
] | [
"#!/usr/bin/env python\nfrom __future__ import division, absolute_import, print_function\nimport numpy as np\nfrom scipy.stats import t\n\n\"\"\"\n Defines common error measures.\n\n \n Definition\n ----------\n def bias(y_obs,y_mod): bias\n def mae(y_obs,y_mod): mean absolute error\n ... | [
[
"numpy.sqrt",
"numpy.isnan",
"numpy.ma.abs",
"numpy.std",
"numpy.mean",
"numpy.ma.array",
"numpy.corrcoef"
],
[
"numpy.array"
],
[
"matplotlib.pyplot.legend",
"numpy.vstack",
"numpy.ma.count",
"scipy.optimize.fmin",
"numpy.ma.array",
"numpy.ma.max",
... |
Davide-DD/distributed-machine-learning-architectures | [
"998d86368c4122ad9937b505405191b316afb060",
"998d86368c4122ad9937b505405191b316afb060"
] | [
"architectures/gossip-learning/nodes/fog-node/code/classes/aged_model.py",
"analyzer/federated_visualize.py"
] | [
"from keras import backend as K\nfrom keras.models import *\nfrom keras.layers import *\nimport os\nfrom datetime import datetime\nimport tensorflow as tf\nimport numpy as np\n\n\nclass AgedModel:\n\n\n\tdef __init__(self, model=None, age=None):\t\t\n\t\tself.graph = tf.Graph()\n\n\t\twith self.graph.as_default():\... | [
[
"tensorflow.Graph",
"tensorflow.Session"
],
[
"matplotlib.pyplot.xlabel",
"pandas.read_csv",
"matplotlib.pyplot.ylabel"
]
] |
mwisnie5/pybaseball | [
"0a2a84d757e478aa79619100872ef48cf7da52c5"
] | [
"pybaseball/teamid_lookup.py"
] | [
"import logging\nimport os\nfrom datetime import date\nfrom typing import Optional\n\nimport pandas as pd\n\nfrom . import lahman\nfrom .datasources import fangraphs\n\n_DATA_FILENAME = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'fangraphs_teams.csv')\n\n\ndef team_ids(season: Optional[int] =... | [
[
"pandas.concat",
"pandas.read_csv"
]
] |
micsthepick/boxed-bottles | [
"424cc0aec3e5d6897a38fc0507d9c609c9f78a1e"
] | [
"object_detection/utils/variables_helper.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.logging.warning",
"tensorflow.train.NewCheckpointReader",
"tensorflow.logging.info"
]
] |
connectthefuture/tensorflow | [
"93812423fcd5878aa2c1d0b68dc0496980c8519d",
"93812423fcd5878aa2c1d0b68dc0496980c8519d",
"93812423fcd5878aa2c1d0b68dc0496980c8519d",
"93812423fcd5878aa2c1d0b68dc0496980c8519d",
"93812423fcd5878aa2c1d0b68dc0496980c8519d",
"93812423fcd5878aa2c1d0b68dc0496980c8519d",
"93812423fcd5878aa2c1d0b68dc0496980c8519... | [
"tensorflow/contrib/seq2seq/python/kernel_tests/seq2seq_test.py",
"tensorflow/python/training/device_setter_test.py",
"tensorflow/python/ops/parsing_ops.py",
"tensorflow/tools/test/run_and_gather_logs_lib.py",
"tensorflow/tensorboard/backend/handler_test.py",
"tensorflow/python/kernel_tests/pool_test.py",... | [
"# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.constant",
"tensorflow.contrib.seq2seq.simple_decoder_fn_inference",
"tensorflow.contrib.layers.linear",
"tensorflow.contrib.seq2seq.dynamic_rnn_decoder",
"tensorflow.test.main",
"tensorflow.constant_initializer",
"tensorflow.global_variables_initializer",
"tensorflow.c... |
ReyhaneAskari/SLA_violation_classification | [
"258a3c415cebcd04601e4d794d42d664471df668",
"258a3c415cebcd04601e4d794d42d664471df668"
] | [
"3_create_database/scripts/createDB2.py",
"4_simple_models/scripts/random_forest_SMOTE_bordeline_1.py"
] | [
"# -*- coding: utf-8 -*-\n\n# In this script we find all the history behind an evicted task that has not been finished/killed/failed/lost.\n# We find how many times it has been submitted and what were the events related to this task. \n# The violatedtasks dictionary is the one that is we are looking for. It has onl... | [
[
"pandas.read_csv"
],
[
"sklearn.cross_validation.cross_val_score",
"matplotlib.pyplot.legend",
"sklearn.cross_validation.train_test_split",
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"matplotlib.pyplot.title",
"matplotlib.use",
"matplotlib.pyplot.ylim",
... |
dg4271/haystack | [
"e930d8a717dfca0c7d502f331d7076b51b5f6898",
"e930d8a717dfca0c7d502f331d7076b51b5f6898"
] | [
"test/benchmarks/retriever.py",
"test/test_retriever.py"
] | [
"import pandas as pd\nfrom pathlib import Path\nfrom time import perf_counter\nfrom utils import get_document_store, get_retriever, index_to_doc_store, load_config\nfrom haystack.preprocessor.utils import eval_data_from_json\nfrom haystack.document_store.faiss import FAISSDocumentStore\n\nfrom haystack import Docum... | [
[
"pandas.DataFrame.from_records"
],
[
"numpy.sum"
]
] |
khawar512/OPVT | [
"690e540e7f54e43751d28a046009993e3e325291",
"690e540e7f54e43751d28a046009993e3e325291"
] | [
"vit_pytorch/do_conv_pytorch.py",
"vit_pytorch/efficient_VIT.py"
] | [
"# coding=utf-8\n\nimport math\nimport torch\nimport numpy as np\nfrom torch.nn import init\nfrom itertools import repeat\nfrom torch.nn import functional as F\nimport collections.abc as container_abcs\nfrom typing import Optional\nfrom torch.nn.parameter import Parameter\nfrom torch.nn.modules.module import Module... | [
[
"torch.nn.init.uniform_",
"torch.Tensor",
"torch.zeros",
"torch.cat",
"torch.einsum",
"torch.nn.functional.conv2d",
"torch.reshape",
"torch.eye",
"torch.from_numpy",
"torch.nn.parameter.Parameter",
"torch.nn.init._calculate_fan_in_and_fan_out",
"numpy.zeros",
"t... |
gunpowder78/google-research | [
"d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5",
"d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5",
"d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5",
"d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5",
"d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5",
"d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5",
"d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e... | [
"unprocessing/process.py",
"tf3d/object_detection/preprocessor_test.py",
"combiner/combiner/tf/approx_attention.py",
"polish/ppo/ppo_model_fn.py",
"scann/configure.py",
"ebp/ebp/common/plot_utils/plot_2d.py",
"aloe/aloe/rfill/seq_embed.py",
"neutra/ebm/train_ebm.py",
"readtwice/models/optimization.p... | [
"# coding=utf-8\n# Copyright 2022 The Google Research Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.compat.v1.ones_like",
"tensorflow.compat.v1.stack",
"tensorflow.compat.v1.image.flip_up_down",
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.reduce_sum",
"tensorflow.compat.v1.space_to_depth",
"tensorflow.compat.v1.image.flip_left_right",
"tensorflow.compat.v1.sh... |
manuelmusngi/machine_learning_algorithms_for_development | [
"f344634f84d8f3a60fbb93892bdaed877855b710",
"f344634f84d8f3a60fbb93892bdaed877855b710",
"f344634f84d8f3a60fbb93892bdaed877855b710"
] | [
"preprocessing_tools.py",
"k_fold_cross_validation.py",
"k_nearest_neighbors.py"
] | [
"# Data Preprocessing Tools for Data Cleaning\n\n# Import libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_ ('')\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, -1].values\nprint(X)\nprint(y)\n\n# Taking care of missing data\nfro... | [
[
"pandas.read_",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.impute.SimpleImputer",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.LabelEncoder"
],
[
"matplotlib.pyplot.legend",
"pandas.read_",
"sklearn.model_se... |
qianrusun1015/Disentangled-Person-Image-Generation | [
"e4703860bb1b351050ce50f339985ff0811f1d64"
] | [
"score_mask.py"
] | [
"from __future__ import print_function\n\nimport os, pdb, sys, glob\n# we need to set GPUno first, otherwise may out of memory\nstage = int(sys.argv[1])\ngpuNO = sys.argv[2]\nmodel_dir = sys.argv[3]\ntest_dir = sys.argv[4]\nos.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=str(g... | [
[
"numpy.product",
"numpy.abs",
"numpy.uint8",
"numpy.std",
"numpy.mean",
"numpy.sum"
]
] |
fogx/docarray | [
"2cb60f893ebcfd29708132e44202ccb20e639c6b"
] | [
"tests/unit/document/test_converters.py"
] | [
"import os\n\nimport numpy as np\nimport pytest\n\nfrom docarray import Document\nfrom docarray.document.generators import from_files\nfrom docarray.helper import __windows__\n\ncur_dir = os.path.dirname(os.path.abspath(__file__))\n\n\ndef test_video_convert_pipe(pytestconfig, tmpdir):\n num_d = 0\n fname = s... | [
[
"numpy.random.RandomState",
"numpy.random.random",
"numpy.random.randint"
]
] |
mevalle/r-DEP-Classifier | [
"c18625fe6b69ec9cc4edead7bb7f647ebf71956a"
] | [
"rDEP.py"
] | [
"import numpy as np\nimport cvxpy as cp\nimport dccp\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nfrom sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin\nfrom sklearn.utils.multiclass import unique_labels\nfrom sklearn.metrics import pairwise_distances\nfrom sklearn.u... | [
[
"matplotlib.pyplot.contourf",
"sklearn.utils.validation.check_is_fitted",
"numpy.max",
"numpy.mean",
"matplotlib.pyplot.gca",
"numpy.arange",
"sklearn.model_selection.StratifiedKFold",
"numpy.zeros",
"matplotlib.pyplot.figure",
"numpy.min",
"matplotlib.pyplot.ylim",
... |
zzdxfei/detectron_cascade_code | [
"59706f1687ee067d1df5da60e7a4a442fb2d59f8"
] | [
"detectron/ops/distribute_cascade_proposals.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicabl... | [
[
"numpy.concatenate",
"numpy.argsort",
"numpy.where",
"numpy.empty"
]
] |
iamchosenlee/MolDQN-pytorch | [
"bda8a74eb9e5d2f3232a6a27b6a32928a3797f6d"
] | [
"dqn.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass MolDQN(nn.Module):\n def __init__(self, input_length, output_length):\n super(MolDQN, self).__init__()\n\n self.linear_1 = nn.Linear(input_length, 1024)\n self.linear_2 = nn.Linear(1024, 512)\n self.linea... | [
[
"torch.nn.Linear",
"torch.nn.ReLU"
]
] |
markqiu/zvt | [
"1bcfb71279f2652c3600f0f8e45d941f98ceaa10",
"23105a5bfdc3a5080c6c22d11e9e53d216688dea"
] | [
"zvt/recorders/joinquant/meta/china_stock_status_recorder.py",
"zvt/recorders/exchange/china_index_list_spider.py"
] | [
"# -*- coding: utf-8 -*-\nimport pandas as pd\nfrom jqdatasdk import auth, logout, finance, query\nfrom zvt.recorders.joinquant.common import to_jq_entity_id\n\nfrom zvt import zvt_env\nfrom zvt.api import TIME_FORMAT_DAY, get_str_schema\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Time... | [
[
"pandas.to_datetime"
],
[
"pandas.concat",
"pandas.DataFrame",
"pandas.read_html"
]
] |
TUM-AIMED/hyfed | [
"06e0ea66a8bf88ecaf09ebc0ff20cdd850d81b7f"
] | [
"hyfed-compensator/hyfed_compensator/project/hyfed_compensator_project.py"
] | [
"\"\"\"\n The main class to obtain the compensation parameters from the clients, aggregate them,\n and share the aggregation results with the server\n\n Copyright 2021 Reza NasiriGerdeh. All Rights Reserved.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this ... | [
[
"numpy.array"
]
] |
muzi2045/second_TANET.pytorch | [
"3e10c93075a76684871fe0f188819c7b282671fd"
] | [
"second/core/non_max_suppression/nms_cpu.py"
] | [
"import math\r\nfrom pathlib import Path\r\nimport numba\r\nimport numpy as np\r\nfrom spconv.spconv_utils import (\r\n non_max_suppression_cpu, rotate_non_max_suppression_cpu)\r\nfrom second.core import box_np_ops\r\nfrom second.core.non_max_suppression.nms_gpu import rotate_iou_gpu\r\n\r\n\r\ndef nms_cc(dets, ... | [
[
"numpy.exp",
"numpy.zeros"
]
] |
MuhammadSYahyaS/cs-flow | [
"bef320ae7b2063f1dce41fb2f2225228cd43a589"
] | [
"train.py"
] | [
"import numpy as np\nimport torch\nfrom sklearn.metrics import roc_auc_score\nfrom tqdm import tqdm\nimport config as c\nfrom model import get_cs_flow_model, save_model, FeatureExtractor, nf_forward\nfrom utils import *\n\n\ndef train(train_loader, test_loader):\n model = get_cs_flow_model()\n optimizer = tor... | [
[
"sklearn.metrics.roc_auc_score",
"numpy.concatenate",
"torch.no_grad",
"numpy.mean",
"numpy.array"
]
] |
sungheeyun/optmlstat | [
"11d529c915bf27976da9157471a6dbf7df34d205"
] | [
"optmlstat/test/test_basic_functions.py"
] | [
"import unittest\nfrom logging import Logger, getLogger\n\nfrom numpy import ndarray, power, allclose\nfrom numpy.random import randn\nfrom freq_used.logging_utils import set_logging_basic_config\n\nfrom optmlstat.functions.function_base import FunctionBase\nfrom optmlstat.functions.example_functions import get_sum... | [
[
"numpy.random.randn",
"numpy.allclose",
"numpy.power"
]
] |
mmderakhshani/ML-From-Scratch | [
"86ccc5273e0182b66c5d93c428f75dad61d8ced3"
] | [
"mlfromscratch/unsupervised_learning/gaussian_mixture_model.py"
] | [
"from __future__ import division, print_function\nimport sys\nimport os\nimport math\nimport random\nfrom sklearn import datasets\nimport numpy as np\n\n# Import helper functions\nfrom mlfromscratch.utils.data_manipulation import normalize\nfrom mlfromscratch.utils.data_operation import euclidean_distance, calculat... | [
[
"numpy.expand_dims",
"numpy.linalg.norm",
"numpy.ones",
"numpy.linalg.det",
"numpy.max",
"numpy.linalg.pinv",
"numpy.shape",
"numpy.zeros",
"numpy.sum",
"sklearn.datasets.make_blobs"
]
] |
EEEGUI/Mapillary-vistas-semseg | [
"d07a107fd08a7536f09f25e426a6f15033cbb609"
] | [
"ptsemseg/loader/mapillary_vistas_loader.py"
] | [
"import os\nimport json\nimport torch\nimport numpy as np\n\nfrom torch.utils import data\nfrom PIL import Image\n\nfrom ptsemseg.utils import recursive_glob\nfrom ptsemseg.augmentations import Compose, RandomHorizontallyFlip, RandomRotate\n\nclass mapillaryVistasLoader(data.Dataset):\n def __init__(\n se... | [
[
"numpy.uint8",
"numpy.array",
"torch.utils.data.DataLoader",
"numpy.zeros"
]
] |
vinay-swamy/gMVP | [
"62202baa0769dfe0e47c230e78dffa42fb1280f1"
] | [
"trainer/trainer.py"
] | [
"import time\nimport json\nimport argparse\nimport os\nimport sys\nimport logging\nimport shutil\nfrom datetime import datetime\nimport glob\nimport random\n\nfrom scipy.stats import mannwhitneyu\nfrom scipy.stats import spearmanr\n\nimport numpy as np\n\nfrom sklearn.metrics import roc_auc_score, precision_recall_... | [
[
"tensorflow.norm",
"tensorflow.config.threading.set_intra_op_parallelism_threads",
"tensorflow.keras.optimizers.schedules.PolynomialDecay",
"tensorflow.summary.histogram",
"tensorflow.zeros",
"sklearn.metrics.auc",
"scipy.stats.spearmanr",
"tensorflow.cast",
"numpy.max",
"s... |
v1259397/cosmic-gnuradio | [
"64c149520ac6a7d44179c3f4a38f38add45dd5dc"
] | [
"gnuradio-3.7.13.4/gr-analog/python/analog/qa_fastnoise.py"
] | [
"#!/usr/bin/env python\n#\n# Copyright 2007,2010,2012 Free Software Foundation, Inc.\n#\n# This file is part of GNU Radio\n#\n# GNU Radio is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 3,... | [
[
"numpy.array_equal"
]
] |
Saiprasad16/transform | [
"774458bf0c296f8275fedf3ace303427654dace7"
] | [
"tensorflow_transform/schema_inference.py"
] | [
"# Copyright 2017 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appl... | [
[
"tensorflow.constant",
"tensorflow.compat.v1.logging.warn",
"tensorflow.io.VarLenFeature",
"tensorflow.python.framework.ops.get_default_graph",
"tensorflow.compat.v1.add_to_collection",
"tensorflow.function"
]
] |
ZzhKlaus/2018-SURF-Trajectory-Estimation | [
"71c62c816d1531f6806bfa9455fec9affe36496c"
] | [
"Data_Processing/BoxPlot_error.py"
] | [
"#By Zhenghang(Klaus) Zhong\n\n#Box Plot of error distribution\n\nfrom pandas import DataFrame\nfrom pandas import read_csv\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot\n# load results into a dataframe\nfilenames_128 = ['dis_diff_128.csv']\nfilenames_256 = ['dis_diff_256.csv']\nfilenames_... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"numpy.concatenate",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] |
python3f/spectral_clustering | [
"bd5900dfa7ada69bd77080b905ef08ea62b420e9"
] | [
"main.py"
] | [
"from sklearn.cluster import KMeans\nfrom sklearn.neighbors import kneighbors_graph\nfrom scipy.spatial.distance import pdist, squareform\nfrom scipy.sparse.csgraph import laplacian\nimport numpy as np\n\n\n\"\"\"Args:\nX: input samples, array (num, dim)\nn_clusters: no. of clusters\nn_neighbours: n... | [
[
"sklearn.cluster.KMeans",
"numpy.linalg.eig",
"sklearn.neighbors.kneighbors_graph",
"scipy.sparse.csgraph.laplacian",
"numpy.argsort"
]
] |
sinzlab/orbit_transfer | [
"812d89af5c7ab26d9ea26766a4250ae023bb20b8",
"812d89af5c7ab26d9ea26766a4250ae023bb20b8"
] | [
"orbit_transfer/models/__init__.py",
"orbit_transfer/dataset/mnist_c/run_generation.py"
] | [
"import torch\nimport numpy as np\nfrom torch.hub import load_state_dict_from_url\n\nfrom nnfabrik.utility.nn_helpers import load_state_dict\n\n\nfrom nntransfer.models.resnet import resnet_builder\nfrom nntransfer.models.utils import get_model_parameters\nfrom nntransfer.models.vgg import vgg_builder\nfrom nntrans... | [
[
"torch.manual_seed",
"torch.hub.load_state_dict_from_url",
"numpy.random.seed",
"torch.zeros"
],
[
"numpy.std",
"numpy.mean"
]
] |
inamori/DeepLearningImplementations | [
"8bbd3c5a4a7d24b2c098ba47cfd45fe2c152771d",
"8bbd3c5a4a7d24b2c098ba47cfd45fe2c152771d",
"8bbd3c5a4a7d24b2c098ba47cfd45fe2c152771d"
] | [
"GAN_tf/src/model/flags.py",
"Colorful/src/utils/general_utils.py",
"WGAN-GP/src/model/train_wgan_GP.py"
] | [
"\nimport tensorflow as tf\n\nFLAGS = tf.app.flags.FLAGS\n\n\ndef define_flags():\n\n ############\n # Run mode\n ############\n tf.app.flags.DEFINE_string('run', None, \"Which operation to run. [train|inference]\")\n\n ##########################\n # Training parameters\n ######################... | [
[
"tensorflow.app.flags.DEFINE_bool",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.app.flags.DEFINE_integer",
"tensorflow.app.flags.DEFINE_float"
],
[
"matplotlib.pylab.tight_layout",
"matplotlib.pylab.show",
"numpy.log",
"numpy.sum",
"numpy.repeat",
"numpy.concatena... |
kwangsungjun/lrbandit | [
"2f1f7ca4bbefe2bfd3e0bc50c4423a9791bfcde8"
] | [
"matrixrecovery/matrixrecovery.py"
] | [
"import myutils_cython\nimport numpy as np, numpy.random as ra, scipy.linalg as sla\nfrom tqdm import tqdm\n\ndef rankone(X,Z,y,r,R=.1, C=.1, tolPred=0.01, tolTh=0.01, maxIter=400, verbose=False):\n \"\"\"\n matrix recovery with rank-one measurements using Burer-Monteiro approach \n measurement model: (X[i... | [
[
"numpy.eye",
"numpy.random.randn",
"scipy.linalg.norm",
"scipy.linalg.solve",
"numpy.zeros"
]
] |
kassyray/NeuroKit | [
"b84d110a71d5d17c0d1efde0d60d00446fda16cb",
"b84d110a71d5d17c0d1efde0d60d00446fda16cb",
"b84d110a71d5d17c0d1efde0d60d00446fda16cb"
] | [
"tests/tests_complexity.py",
"neurokit2/rsp/rsp_process.py",
"neurokit2/signal/signal_findpeaks.py"
] | [
"import numpy as np\nimport pandas as pd\nimport neurokit2 as nk\nimport nolds\n\nfrom pyentrp import entropy as pyentrp\n\n\"\"\"\nFor the testing of complexity, we test our implementations against existing and established ones.\nHowever, some of these other implementations are not really packaged in a way\nSO THA... | [
[
"numpy.linspace",
"numpy.asarray",
"sklearn.neighbors.KDTree",
"numpy.lib.stride_tricks.as_strided",
"numpy.max",
"numpy.mean",
"numpy.fill_diagonal",
"numpy.var",
"numpy.divide",
"pandas.read_csv",
"numpy.allclose",
"numpy.subtract",
"numpy.std",
"numpy.cop... |
shangz-ai/transformers | [
"75259b44bf2e2b98b5a4d431fb400b7190342a01",
"75259b44bf2e2b98b5a4d431fb400b7190342a01",
"75259b44bf2e2b98b5a4d431fb400b7190342a01",
"75259b44bf2e2b98b5a4d431fb400b7190342a01",
"75259b44bf2e2b98b5a4d431fb400b7190342a01",
"75259b44bf2e2b98b5a4d431fb400b7190342a01"
] | [
"tests/models/tapas/test_tokenization_tapas.py",
"tests/models/big_bird/test_modeling_big_bird.py",
"src/transformers/models/longformer/modeling_longformer.py",
"src/transformers/models/xlm/modeling_xlm.py",
"tests/models/lxmert/test_modeling_lxmert.py",
"src/transformers/models/wav2vec2_conformer/modelin... | [
"# coding=utf-8\n# Copyright 2020 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requir... | [
[
"pandas.DataFrame.from_records",
"numpy.array",
"torch.no_grad",
"pandas.DataFrame.from_dict"
],
[
"torch.Size",
"torch.ones",
"torch.cat",
"torch.zeros",
"torch.manual_seed",
"torch.einsum",
"torch.tensor",
"torch.no_grad",
"torch.allclose",
"torch.ones... |
Haiper-ai/kubric | [
"d096ba178b8a78ea2c840ae121646d36281d31d9"
] | [
"challenges/multiview_matting/worker.py"
] | [
"# Copyright 2021 The Kubric Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or ... | [
[
"numpy.dot",
"numpy.cos",
"numpy.linalg.norm",
"numpy.random.RandomState",
"numpy.vstack"
]
] |
KiLJ4EdeN/tf2_nn | [
"0ccec7692f061e7e066a4a2439683e3b09faa7bc"
] | [
"tfnn_mlp.py"
] | [
"import tensorflow as tf\nimport matplotlib.pyplot as plt\n\n# MNIST dataset parameters.\nnum_classes = 10 # 0 to 9 digits\nnum_features = 784 # 28*28\n\n# Training parameters.\nlearning_rate = 0.001\ntraining_steps = 1000\nbatch_size = 256\ndisplay_step = 100\n\n# Network parameters.\nn_hidden_1 = 128 # 1st layer ... | [
[
"tensorflow.clip_by_value",
"tensorflow.matmul",
"tensorflow.nn.softmax",
"tensorflow.Variable",
"tensorflow.zeros",
"tensorflow.initializers.RandomNormal",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.keras.datasets.mni... |
kevin3/cwl-ica | [
"cf706ea42993d563f364c0847ee4b882f8fe067c",
"cf706ea42993d563f364c0847ee4b882f8fe067c",
"cf706ea42993d563f364c0847ee4b882f8fe067c"
] | [
"src/subcommands/listers/list_users.py",
"src/subcommands/listers/list_projects.py",
"src/subcommands/listers/list_tenants.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"\nList all users registered in <CWL_ICA_REPO_PATH>/config/user.yaml\n\"\"\"\n\nfrom classes.command import Command\nfrom utils.logging import get_logger\nimport pandas as pd\nfrom utils.repo import read_yaml, get_user_yaml_path\nimport sys\n\nlogger = get_logger()\n\n\nclass ListUse... | [
[
"pandas.DataFrame"
],
[
"pandas.DataFrame"
],
[
"pandas.DataFrame"
]
] |
Broly498/sentinel2-cloud-detector | [
"912880fcd6fed482475b4cd8da07bda17993ebe8"
] | [
"examples/plotting_utils.py"
] | [
"\"\"\"\nPlotting utilities for example notebooks\n\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef plot_image(image=None, mask=None, ax=None, factor=3.5/255, clip_range=(0, 1), **kwargs):\n \"\"\" Utility function for plotting RGB images and masks.\n \"\"\"\n if ax is None:\n _,... | [
[
"numpy.clip",
"numpy.asarray",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.subplot",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] |
henrykironde/DeepForest | [
"9df98ea30debc8a1dc98edfa45dada063b109e6e"
] | [
"deepforest/preprocess.py"
] | [
"# Deepforest Preprocessing model\n\"\"\"The preprocessing module is used to reshape data into format suitable for\ntraining or prediction.\n\nFor example cutting large tiles into smaller images.\n\"\"\"\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport slidingwindow\nfrom PIL import Image\nimport torch... | [
[
"pandas.concat",
"pandas.read_csv",
"torch.tensor",
"numpy.moveaxis",
"numpy.array"
]
] |
furkannturkmen/CS231n-2021 | [
"2c6618d16bfd4e02e0493e8b8a411a6509206bb4"
] | [
"assignment2/cs231n/optim.py"
] | [
"import numpy as np\n\n\"\"\"\nThis file implements various first-order update rules that are commonly used\nfor training neural networks. Each update rule accepts current weights and the\ngradient of the loss with respect to those weights and produces the next set of\nweights. Each update rule has the same interfa... | [
[
"numpy.zeros_like",
"numpy.sqrt"
]
] |
kaikun213/fonduer-troy200 | [
"d5653df48e3ce3037f4f1b500d454947ad0d010c"
] | [
"src/fonduer_utils.py"
] | [
"import emmental\nimport numpy as np\n\nfrom fonduer import Meta\nfrom emmental.modules.embedding_module import EmbeddingModule\nfrom emmental.data import EmmentalDataLoader\nfrom emmental.model import EmmentalModel\nfrom emmental.learner import EmmentalLearner\nfrom fonduer.learning.utils import collect_word_count... | [
[
"numpy.array"
]
] |
neptune-ai/examples | [
"e64cfaadb028e2187063fc43768dfee44074729b"
] | [
"integrations-and-supported-tools/optuna/scripts/Neptune_Optuna_integration_customize_callback.py"
] | [
"import lightgbm as lgb\nimport neptune.new as neptune\nimport neptune.new.integrations.optuna as optuna_utils\nimport optuna\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import train_test_split\n\n\ndef objective(trial):\n data, target... | [
[
"sklearn.metrics.roc_auc_score",
"sklearn.model_selection.train_test_split",
"sklearn.datasets.load_breast_cancer"
]
] |
mttgdd/liegroups | [
"ca637bd461300d70d70f90bff7a18462d06f5f82"
] | [
"liegroups/torch/se2.py"
] | [
"import torch\n\nfrom . import _base\nfrom . import utils\nfrom .so2 import SO2Matrix\n\n\nclass SE2Matrix(_base.SEMatrixBase):\n \"\"\"See :mod:`liegroups.SE2`\"\"\"\n dim = 3\n dof = 3\n RotationType = SO2Matrix\n\n def adjoint(self):\n rot_part = self.rot.as_matrix()\n if rot_part.di... | [
[
"torch.cat",
"torch.zeros",
"torch.sin",
"torch.eye",
"torch.bmm",
"torch.cos"
]
] |
972d5defe3218bd62b741e6a2f11f5b3/riptable | [
"bb928c11752e831ec701f91964979b31db53826a",
"bb928c11752e831ec701f91964979b31db53826a",
"bb928c11752e831ec701f91964979b31db53826a",
"bb928c11752e831ec701f91964979b31db53826a"
] | [
"riptable/tests/test_scalar.py",
"riptable/tests/test_accum2.py",
"riptable/hypothesis_tests/test_riptide_numpy_equivalency.py",
"riptable/benchmarks/bench_merge.py"
] | [
"\"\"\"Test around scalar constructors and scalar methods.\"\"\"\r\nimport riptable as rt\r\nimport numpy as np\r\nimport pytest\r\n\r\nfrom numpy.testing import assert_almost_equal, assert_warns\r\n\r\n\r\nclass TestScalarConstructor(object):\r\n # Type-coercion from strings test cases adapted from numpy/core/t... | [
[
"numpy.testing.assert_almost_equal"
],
[
"pandas.util.testing.makeTimeDataFrame",
"numpy.testing.assert_almost_equal",
"pandas.DataFrame"
],
[
"numpy.minimum",
"numpy.sqrt",
"numpy.cumsum",
"numpy.nan_to_num",
"numpy.dtype",
"numpy.all",
"numpy.concatenate",
... |
vik748/OpenSfM | [
"bd949246e3e0d6d3a707a08224038034d27e3ee8",
"569144c26df860cfa45d183f7701d0414e35d086"
] | [
"scripts/track_length_analysis_test.py",
"opensfm/test/test_triangulation.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 19 08:11:51 2021\n\n@author: vik748\n\"\"\"\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys,os\nimport pandas as pd\n\ndef tracks_histogram(recon_file, tracks_file, ax, model_num=0, bins=np.linspace(2,15,14)):... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"numpy.linspace",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.MultipleLocator",
"matplotlib.pyplot.show"
],
[
"numpy.radians",
"numpy.allclose",
"numpy.linalg.norm",
"numpy.identity",
"numpy.array"
]
] |
prkkumar/WarpX | [
"a83c9c03ecc9850cd724efc14075eb95ff8a6138"
] | [
"Python/pywarpx/fields.py"
] | [
"# Copyright 2017-2019 David Grote\n#\n# This file is part of WarpX.\n#\n# License: BSD-3-Clause-LBNL\n\n\"\"\"Provides wrappers around field and current density on multiFABs\n\nAvailable routines:\n\nExWrapper, EyWrapper, EzWrapper\nBxWrapper, ByWrapper, BzWrapper\nJxWrapper, JyWrapper, JzWrapper\n\n\"\"\"\nimport... | [
[
"numpy.arange",
"numpy.array",
"numpy.zeros_like",
"numpy.zeros"
]
] |
brianwa84/probability | [
"6f8e78d859ac41170be5147c8c7bde54cc5aa83e",
"6f8e78d859ac41170be5147c8c7bde54cc5aa83e",
"6f8e78d859ac41170be5147c8c7bde54cc5aa83e",
"6f8e78d859ac41170be5147c8c7bde54cc5aa83e",
"6f8e78d859ac41170be5147c8c7bde54cc5aa83e",
"6f8e78d859ac41170be5147c8c7bde54cc5aa83e",
"6f8e78d859ac41170be5147c8c7bde54cc5aa83... | [
"tensorflow_probability/python/distributions/skellam_test.py",
"tensorflow_probability/python/distributions/lognormal_test.py",
"tensorflow_probability/python/internal/tensor_util.py",
"spinoffs/inference_gym/inference_gym/targets/vector_model.py",
"spinoffs/oryx/oryx/util/summary_test.py",
"spinoffs/infe... | [
"# Copyright 2018 The TensorFlow Probability Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by a... | [
[
"scipy.stats.skellam.logpmf",
"scipy.stats.skellam.mean",
"tensorflow.compat.v2.Variable",
"tensorflow.compat.v2.test.main",
"tensorflow.compat.v2.constant",
"numpy.linspace",
"numpy.sqrt",
"tensorflow.compat.v2.GradientTape",
"scipy.stats.skellam.pmf",
"tensorflow.compat.v... |
Pandinosaurus/model-optimization | [
"12dc84dd34ee3c6eb08b381c0abcd65b31a42366",
"12dc84dd34ee3c6eb08b381c0abcd65b31a42366"
] | [
"tensorflow_model_optimization/python/core/quantization/keras/collaborative_optimizations/cluster_preserve/cluster_preserve_quantize_registry.py",
"tensorflow_model_optimization/python/core/clustering/keras/cluster_wrapper.py"
] | [
"# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.multiply",
"tensorflow.unique",
"tensorflow.shape",
"tensorflow.reduce_any",
"tensorflow.reshape",
"tensorflow.one_hot",
"tensorflow.math.divide_no_nan",
"tensorflow.size",
"tensorflow.abs"
],
[
"tensorflow.math.multiply",
"tensorflow.math.not_equal",
... |
Luciano233/OCR_Japanease | [
"055bdd0cc8e4d053dfb471cd642b1616ba0938d1"
] | [
"nets/classifiernet.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .block import Mish, SeparableConv2d, Block\n\nclass WideTipXception(nn.Module):\n def __init__(self, num_class):\n super(WideTipXception, self).__init__()\n\n self.conv1 = nn.Conv2d(1, 192, 3, 2, 1, bias=True)\n se... | [
[
"torch.nn.Linear",
"torch.nn.Conv2d",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.nn.BatchNorm2d"
]
] |
HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping | [
"1e2dee8d6d1f97722eba91618462537faf9efba7"
] | [
"InLine_Implementation/Code/utils/fftutils.py"
] | [
"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport torch\n\n################################################################################\n################################################################################\... | [
[
"numpy.log2",
"numpy.sqrt",
"numpy.linspace",
"torch.cat",
"numpy.reshape",
"numpy.cos",
"numpy.arctan2",
"torch.unbind",
"torch.stack",
"numpy.meshgrid"
]
] |
natemalek/molen-pater-nathan-rma-thesis-coreference-with-singletons | [
"3d2d6c751eadd6438a80b0c24f48b2635bc6acc7"
] | [
"deep-coref/modified_keras/examples/babi_memnn.py"
] | [
"from __future__ import print_function\nfrom keras.models import Sequential\nfrom keras.layers.embeddings import Embedding\nfrom keras.layers.core import Activation, Dense, Merge, Permute, Dropout\nfrom keras.layers.recurrent import LSTM\nfrom keras.datasets.data_utils import get_file\nfrom keras.preprocessing.sequ... | [
[
"numpy.array"
]
] |
TatsuyaShirakawa/torchemb | [
"b72912c7602537f368c16fdeb2bb6cf177b742be"
] | [
"scripts/poincare_embeddings.py"
] | [
"'''\nA rouch implementation of the following paper\n\nM. Nickel+, \"Poincaré Embeddings for Learning Hierarchical Representations\", NeurIPS2017\nhttps://arxiv.org/pdf/1705.08039.pdf\n'''\nimport argparse\nfrom pathlib import Path\nimport itertools\nimport math\nimport numpy as np\nimport torch\nimport torch.nn as... | [
[
"torch.nn.CrossEntropyLoss",
"torch.LongTensor",
"torch.isnan",
"numpy.random.seed",
"torch.manual_seed",
"torch.no_grad"
]
] |
kbots-dga/ml_classificator | [
"0163141de7389825787b9813019582be98d0f266"
] | [
"app.py"
] | [
"import streamlit as st\nimport plotly.express as px\nimport pandas as pd\nimport pickle\nimport os\nimport base64\nfrom io import BytesIO\nfrom datetime import datetime\n\n\ndef to_excel(df):\n output = BytesIO()\n writer = pd.ExcelWriter(output, engine='xlsxwriter')\n df.to_excel(writer, index=False, she... | [
[
"pandas.read_excel",
"pandas.read_csv",
"pandas.ExcelWriter"
]
] |
tomkimpson/ML4L | [
"ffa8360cb80df25bd6af4fa5cc39b42bd6f405cd",
"ffa8360cb80df25bd6af4fa5cc39b42bd6f405cd"
] | [
"legacy/legacy_scripts/legacy/.ipynb_checkpoints/train_and_predict-checkpoint.py",
"pipeline/raw_data_processor/prep_for_ML.py"
] | [
"\n\n\n\n\nimport tensorflow as tf\nimport os\nimport time\nimport json\nimport pandas as pd\n\n\n\n\"\"\"\nScript to train a sequential NN.\nTakes a df, filters based on `condition` (default None), and separates into test/train based on time\nNN trains on training data, all results output to disk\n\"\"\"\n\n\ndef ... | [
[
"tensorflow.keras.layers.Dense",
"pandas.read_pickle",
"tensorflow.config.list_physical_devices",
"tensorflow.keras.layers.Normalization"
],
[
"pandas.read_parquet",
"pandas.concat",
"pandas.merge"
]
] |
paul-ang/nas-segm-pytorch | [
"e83704b6cdac6426d6ee51059f82cf650238677c",
"e83704b6cdac6426d6ee51059f82cf650238677c"
] | [
"src/utils/solvers.py",
"src/data/hsi_dataset.py"
] | [
"\"\"\"Initialising Optimisers\"\"\"\n\nimport torch\n\n\ndef create_optimisers(\n optim_enc,\n optim_dec,\n lr_enc,\n lr_dec,\n mom_enc,\n mom_dec,\n wd_enc,\n wd_dec,\n param_enc,\n param_dec,\n):\n \"\"\"Create optimisers for encoder, decoder\n\n Args:\n optim_enc (str) :... | [
[
"torch.optim.Adam",
"torch.optim.SGD"
],
[
"torch.Tensor",
"numpy.min",
"numpy.concatenate",
"numpy.max",
"numpy.argmax",
"numpy.float32",
"numpy.moveaxis",
"numpy.argsort",
"numpy.zeros",
"numpy.where",
"numpy.loadtxt",
"torch.as_tensor"
]
] |
rpalovics/Alpenglow | [
"7a15d5c57b511787379f095e7310e67423159fa0"
] | [
"examples/external_models/turicreate/run_turicreate.py"
] | [
"import os\nos.environ[\"OMP_NUM_THREADS\"] = \"10\"\n\nimport sys\nimport pandas as pd\nimport numpy as np\nimport turicreate as tc\n\n\nfor i in range(1, 14):\n print(\"running batch %d\" % i)\n batch = pd.read_csv(\"batches/batch_%d_train.dat\" % i)\n test_users = pd.read_csv(\"batches/batch_%d_test.dat... | [
[
"pandas.read_csv"
]
] |
smarsu/facenet | [
"a0fa3ffe32e295b4cc980a4a178593cc7f1bad12"
] | [
"compare.py"
] | [
"# --------------------------------------------------------\n# SMNet FaceNet\n# Licensed under The MIT License [see LICENSE for details]\n# Copyright 2019 smarsu. All Rights Reserved.\n# --------------------------------------------------------\n\nimport os.path as osp\nimport numpy as np\nfrom sklearn import metric... | [
[
"numpy.square",
"sklearn.metrics.roc_auc_score",
"sklearn.metrics.roc_curve",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"numpy.array",
"numpy.sum"
]
] |
Ombarus/python_env | [
"4615976a51aa4f02206f5e03fc091b088d3273fc",
"4615976a51aa4f02206f5e03fc091b088d3273fc"
] | [
"python35/Lib/site-packages/sklearn/linear_model/base.py",
"python35/Lib/site-packages/sklearn/linear_model/ridge.py"
] | [
"\"\"\"\nGeneralized Linear models.\n\"\"\"\n\n# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Fabian Pedregosa <fabian.pedregosa@inria.fr>\n# Olivier Grisel <olivier.grisel@ensta.org>\n# Vincent Michel <vincent.michel@inria.fr>\n# Peter Prettenhofer <peter.prettenhofer@gmail.com>\n# ... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.iinfo",
"numpy.exp",
"scipy.sparse.dia_matrix",
"scipy.sparse.issparse",
"scipy.linalg.lstsq",
"numpy.atleast_1d",
"numpy.ravel",
"numpy.reciprocal",
"numpy.zeros",
"scipy.sparse.csc_matrix",
"scipy.sparse.csr_matrix",
"nump... |
StephenLouis/ISIC_2019 | [
"340ece42915c770e68bc13da64698a7a8987420e"
] | [
"Data_Loader.py"
] | [
"import os\nimport torch\nimport csv\nimport numpy as np\nfrom torch.utils.data import Dataset\nfrom PIL import Image\n\ndef split_csv(file):\n data = []\n a_train_file = r'/home/huangyinyue/ISIC_2019/train.csv'\n a_test_file = r'/home/huangyinyue/ISIC_2019/test.csv'\n\n seed = 3\n np.random.seed(see... | [
[
"numpy.random.seed",
"numpy.random.choice",
"numpy.asarray",
"torch.from_numpy",
"numpy.array"
]
] |
daniyaljamal/Personality-prediction-based-on-video-using-CNN | [
"0f1052d09fe14c73e38ac529ad35e4e98a8d859e"
] | [
"data preprocessing/MTCNN2.py"
] | [
"# extract and plot each detected face in a photograph\nfrom facenet_pytorch import MTCNN\nfrom cv2 import cv2\nfrom PIL import Image\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom tqdm.notebook import tqdm\nimport os\nimport tensorflow as tf\nfrom torchvision import models\nimport torch\nfrom torc... | [
[
"matplotlib.pyplot.axis"
]
] |
Igor-ID/Image-Compression | [
"e54881b62f258260baa7036cdd3b264b0d8adf05"
] | [
"wavelet_compress.py"
] | [
"import pywt\nimport matplotlib.pyplot as plt\nfrom matplotlib.image import imread\nimport numpy as np\n\n\"\"\"Image compression using discrete Wavelet transform.\"\"\"\n\nplt.rcParams['figure.figsize'] = [8, 8]\nplt.rcParams.update({'font.size': 18})\n\nim = imread('data/dog.jpg')\nim_gray = np.mean(im, -1) # co... | [
[
"numpy.abs",
"matplotlib.image.imread",
"numpy.mean",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
drigols/Studies | [
"9c293156935b491ded24be6b511daac67fd43538",
"9c293156935b491ded24be6b511daac67fd43538",
"9c293156935b491ded24be6b511daac67fd43538",
"9c293156935b491ded24be6b511daac67fd43538",
"9c293156935b491ded24be6b511daac67fd43538",
"9c293156935b491ded24be6b511daac67fd43538"
] | [
"modules/ai-codes/modules/knn/src/iris-v1.py",
"modules/math-codes/modules/linear-algebra/matrices/src/sparsity.py",
"modules/math-codes/modules/linear-algebra/vectors/src/vector_addition.py",
"modules/ai-codes/modules/logistic-regression/src/deodorant_instant_liking_gridSearchCV.py",
"modules/ai-codes/modu... | [
"########################################################\n# Rodrigo Leite - drigols #\n# Last update: 31/10/2021 #\n########################################################\n\nfrom sklearn.datasets import load_iris\nimport pandas as pd\n\niris = load_iris()... | [
[
"sklearn.datasets.load_iris",
"pandas.Series",
"pandas.DataFrame"
],
[
"numpy.count_nonzero",
"numpy.array",
"scipy.sparse.csr_matrix"
],
[
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.quiver",
"matplotlib.p... |
tableClothed/face-filters | [
"8b236643b4e22a925df6a1c299f3887fdedb3e8e"
] | [
"flask/camera.py"
] | [
"import cv2\nimport numpy as np\nimport dlib\nfrom imutils import face_utils, translate\n\nclass Camera(object):\n\tdef __init__(self):\n\t\tself.camera = cv2.VideoCapture(0)\n\n\t\tp = \"../data/shape_predictor_68_face_landmarks.dat\"\n\t\tself.detector = dlib.get_frontal_face_detector()\n\t\tself.predictor = dlib... | [
[
"numpy.hstack",
"numpy.linspace",
"numpy.median",
"numpy.zeros_like",
"numpy.mean",
"numpy.array",
"numpy.zeros"
]
] |
RudyVenguswamy/DALI | [
"1456689cbb06a6d6f2c46c3fd231d1c296808e00"
] | [
"dali/test/python/test_operator_gaussian_blur.py"
] | [
"# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless req... | [
[
"numpy.uint8",
"numpy.array",
"numpy.float32"
]
] |
stephenpascoe/arrow | [
"3efd08f0cbaa40d0d3a329b8613fb80ac022b985"
] | [
"python/pyarrow/tests/test_convert_pandas.py"
] | [
"# -*- coding: utf-8 -*-\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.... | [
[
"pandas.to_datetime",
"pandas.Series",
"numpy.sqrt",
"numpy.random.random_sample",
"pandas.DataFrame",
"numpy.dtype",
"pandas.util.testing.assert_frame_equal",
"numpy.random.randn",
"numpy.iinfo",
"numpy.bool_",
"numpy.random.randint",
"numpy.testing.assert_equal",
... |
msenosain/TMA36_dataanalysis | [
"ba390b40e9ffb2bf8ec39b3bd6e8aa000174c313"
] | [
"src/data_integration/pw_corr.py"
] | [
"import pandas as pd\nimport pingouin as pg\n\ndef pw_corr(data_path=\"data/TMA36_project/Radiomics/processed/rad_healthmyne.csv\", \n cde_path=\"data/TMA36_project/CDE/CDE_TMA36_2020FEB25_SA_MF.csv\"):\n rad_hm = pd.read_csv(data_path, index_col=0)\n cde = pd.read_csv(cde_path, index_col=1)\n c... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.DataFrame"
]
] |
kyuhyoung/grasping-invisible | [
"2aaaeb9e28995628ec038a79496453be9f26ffff",
"2aaaeb9e28995628ec038a79496453be9f26ffff"
] | [
"utils.py",
"policies.py"
] | [
"import math\n\nimport numpy as np\nfrom skimage.morphology.convex_hull import convex_hull_image\nfrom scipy.ndimage.morphology import binary_dilation\n\n\ndef check_grasp_margin(target_mask_heightmap, depth_heightmap):\n margin_mask = binary_dilation(target_mask_heightmap, iterations=10).astype(np.float32)-targ... | [
[
"numpy.dot",
"scipy.ndimage.morphology.binary_dilation",
"numpy.linspace",
"numpy.asarray",
"numpy.concatenate",
"numpy.round",
"numpy.sin",
"numpy.argmax",
"numpy.zeros",
"numpy.random.power",
"numpy.multiply",
"numpy.isnan",
"numpy.deg2rad",
"numpy.floor",... |
xiebaiyuan/PaddleLite | [
"6f7280a91741d1c63fcb0296ac5c08c4e81c2a90"
] | [
"lite/tests/unittest_py/auto_scan_base.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.array",
"numpy.allclose",
"numpy.random.seed"
]
] |
jdehotin/TensorFlow | [
"a6c5f8e4e013e54fed8dfcf49fb6de365f018022",
"a6c5f8e4e013e54fed8dfcf49fb6de365f018022",
"a6c5f8e4e013e54fed8dfcf49fb6de365f018022",
"a6c5f8e4e013e54fed8dfcf49fb6de365f018022",
"a6c5f8e4e013e54fed8dfcf49fb6de365f018022",
"a6c5f8e4e013e54fed8dfcf49fb6de365f018022",
"a6c5f8e4e013e54fed8dfcf49fb6de365f01802... | [
"tensorflow/python/summary/impl/io_wrapper.py",
"tensorflow/contrib/distributions/python/ops/dirichlet_multinomial.py",
"tensorflow/contrib/slim/python/slim/evaluation_test.py",
"tensorflow/contrib/layers/python/layers/feature_column_ops_test.py",
"tensorflow/python/client/session_test.py",
"tensorflow/co... | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.summary.impl.gcs_file_loader.GCSFileLoader",
"tensorflow.python.platform.gfile.Walk",
"tensorflow.python.platform.gfile.ListDirectory",
"tensorflow.python.summary.impl.gcs.IsDirectory",
"tensorflow.python.summary.impl.gcs.IsGCSPath",
"tensorflow.python.summary.impl.event... |
ahoneybun/tensorflow | [
"51100a8de57ef53e36a8a9f5a9829cbd33fbed04",
"5134e65300d1ac384eeb1f4ca72a011ad7225bc8",
"5134e65300d1ac384eeb1f4ca72a011ad7225bc8",
"5134e65300d1ac384eeb1f4ca72a011ad7225bc8"
] | [
"tensorflow/python/keras/engine/training.py",
"tensorflow/python/keras/applications/imagenet_utils_test.py",
"tensorflow/compiler/tests/eager_test.py",
"tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.keras.engine.training_eager.predict_loop",
"numpy.expand_dims",
"tensorflow.python.keras.backend.name_scope",
"tensorflow.python.keras.engine.training_eager.test_on_batch",
"numpy.asarray",
"tensorflow.python.keras.engine.training_utils.prepare_sample_weights",
"tens... |
jkrogager/PyNOT | [
"2514a443079e50c12a13ebbd89a48f91a8d20626"
] | [
"pynot/phot.py"
] | [
"\"\"\"\nFunctions for Imaging Pipeline\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Ellipse\nfrom astropy.io import fits\nfrom astropy.modeling import models, fitting\nfrom astropy.table import Table\nfrom scipy.optimize import curve_fit\nimport os\n\nfrom astropy.... | [
[
"numpy.nanmax",
"numpy.nanmedian",
"numpy.sqrt",
"numpy.nanmin",
"numpy.round",
"numpy.max",
"numpy.argmin",
"numpy.nanmean",
"numpy.zeros_like",
"numpy.mean",
"scipy.optimize.curve_fit",
"numpy.arange",
"numpy.std",
"numpy.nansum",
"matplotlib.pyplot.fi... |
RobinVogel/metric-learn | [
"a30471424d35b0ef47582751fa6acea7b3a3bce5",
"a30471424d35b0ef47582751fa6acea7b3a3bce5"
] | [
"test/metric_learn_test.py",
"metric_learn/nca.py"
] | [
"import unittest\nimport re\nimport pytest\nimport numpy as np\nimport scipy\nfrom scipy.optimize import check_grad, approx_fprime\nfrom six.moves import xrange\nfrom sklearn.metrics import pairwise_distances, euclidean_distances\nfrom sklearn.datasets import (load_iris, make_classification, make_regression,\n ... | [
[
"numpy.diag",
"numpy.dot",
"sklearn.datasets.make_classification",
"numpy.random.randn",
"numpy.zeros_like",
"numpy.fill_diagonal",
"numpy.where",
"sklearn.datasets.make_spd_matrix",
"numpy.random.randint",
"numpy.ones_like",
"numpy.unique",
"numpy.arange",
"num... |
WeatherGod/numpy | [
"5be45b280b258e158b93163b937f8f9c08d30393",
"5be45b280b258e158b93163b937f8f9c08d30393",
"5be45b280b258e158b93163b937f8f9c08d30393",
"5be45b280b258e158b93163b937f8f9c08d30393",
"5be45b280b258e158b93163b937f8f9c08d30393"
] | [
"numpy/ma/tests/test_regression.py",
"numpy/distutils/fcompiler/absoft.py",
"numpy/random/__init__.py",
"numpy/linalg/setup.py",
"numpy/distutils/fcompiler/pg.py"
] | [
"from __future__ import division, absolute_import, print_function\n\nimport numpy as np\nimport numpy.ma as ma\nfrom numpy.testing import *\nfrom numpy.compat import sixu\n\nrlevel = 1\n\nclass TestRegression(TestCase):\n def test_masked_array_create(self,level=rlevel):\n \"\"\"Ticket #17\"\"\"\n x... | [
[
"numpy.ma.nonzero",
"numpy.arange",
"numpy.atleast_2d",
"numpy.ma.zeros",
"numpy.compat.sixu",
"numpy.ma.masked_array",
"numpy.ma.array",
"numpy.ma.masked_where",
"numpy.zeros"
],
[
"numpy.distutils.fcompiler.FCompiler.get_libraries",
"numpy.distutils.fcompiler.FCom... |
zhanyinx/SPT_analysis | [
"1cf806c1fd6051e7fc998d2860a16bea6aa9de1a"
] | [
"source/spot_detection_tracking/trackmate_xml_2d.py"
] | [
"\"\"\"\\U0001F1EB\\U0001F1EF \\U00002B50 CSV track coordinate to TrackMate XML conversion.\nFiji allows for quick and easy viewing of images. TrackMate can be used to view tracks.\nUnfortunately, it isn't that simple to convert \"normal\" coordinate output into\nTrackMate-viewable format.\nRequires a \"tracks.csv... | [
[
"numpy.expand_dims",
"numpy.setdiff1d"
]
] |
PatrykNeubauer/NeMo | [
"3ada744b884dba5f233f22c6991fc6092c6ca8d0",
"3ada744b884dba5f233f22c6991fc6092c6ca8d0"
] | [
"scripts/asr_language_modeling/neural_rescorer/eval_neural_rescorer.py",
"nemo/collections/nlp/models/text_classification/text_classification_model.py"
] | [
"# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"numpy.linspace",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.sum",
"matplotlib.pyplot.plot",
"numpy.round",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"matplotlib.pyplot.xlabel",
"numpy.array... |
mattzett/numerical_electromagnetics | [
"07634817ba854a5515c8c31545b735f651878c5e"
] | [
"magnetic_diffusion/diffusion1D.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb 21 19:43:50 2022\n\nIllustrating a basic transient magnetic diffusion problem, See Jackson Section 5.18\n\n@author: zettergm\n\"\"\"\n\nimport numpy as np\nimport scipy.sparse.linalg\nimport scipy.sparse\nfrom scipy.special import erf\nimp... | [
[
"matplotlib.pyplot.legend",
"numpy.abs",
"numpy.linspace",
"matplotlib.pyplot.title",
"numpy.reshape",
"matplotlib.pyplot.ylim",
"numpy.sqrt",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot... |
Yang-YiFan/DiracDeltaNet | [
"36487542422d7573fec6e852b9eece18c6cbce21"
] | [
"extensions/utils.py"
] | [
"'''Some helper functions for PyTorch, including:\r\n - get_mean_and_std: calculate the mean and std value of dataset.\r\n - msr_init: net parameter initialization.\r\n - progress_bar: progress bar mimic xlua.progress.\r\n'''\r\nimport os\r\nimport sys\r\nimport time\r\nimport math\r\n\r\nimport torch\r\ni... | [
[
"torch.nn.init.kaiming_normal",
"torch.zeros",
"numpy.clip",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"numpy.ones",
"torch.nn.init.normal",
"torch.nn.init.constant",
"numpy.random.randint"
]
] |
XinYao1994/mindspore | [
"2c1a2bf752a1fde311caddba22633d2f4f63cb4e"
] | [
"tests/ut/python/ops/test_tensor_slice.py"
] | [
"# Copyright 2020 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable l... | [
[
"numpy.arange",
"numpy.random.uniform",
"numpy.array",
"numpy.ones"
]
] |
Strasser-Pablo/pipelines | [
"a1d513eb412f3ffd44edf82af2fa7edb05c3b952"
] | [
"components/deprecated/tfx/ExampleGen/CsvExampleGen/with_URI_IO/component.py"
] | [
"from typing import NamedTuple\n\ndef CsvExampleGen(\n output_examples_uri: 'ExamplesUri',\n input_base: str,\n input_config: {'JsonObject': {'data_type': 'proto:tfx.components.example_gen.Input'}},\n output_config: {'JsonObject': {'data_type': 'proto:tfx.components.example_gen.Output'}},\n range_con... | [
[
"tensorflow.io.gfile.listdir"
]
] |
AcudoDev/FinanceToolbox | [
"90676e798f2e8eac164ccfcd6708cc717e1911f2"
] | [
"Other/GaussianRandomStockPrice.py"
] | [
"import pandas as pd\nimport numpy as np\n\nimport yfinance as yf\nfrom sklearn.linear_model import LinearRegression\nimport statsmodels\nimport statsmodels.api as sm\nimport statsmodels.tsa.stattools as ts\n\nimport datetime\n\nimport scipy.stats\nimport math\nimport openpyxl as pyxl\nfrom scipy import signal\nfro... | [
[
"numpy.random.normal",
"pandas.DataFrame",
"pandas.date_range"
]
] |
UCLA-SEAL/QDiff | [
"d968cbc47fe926b7f88b4adf10490f1edd6f8819",
"d968cbc47fe926b7f88b4adf10490f1edd6f8819",
"d968cbc47fe926b7f88b4adf10490f1edd6f8819",
"d968cbc47fe926b7f88b4adf10490f1edd6f8819",
"d968cbc47fe926b7f88b4adf10490f1edd6f8819",
"d968cbc47fe926b7f88b4adf10490f1edd6f8819",
"d968cbc47fe926b7f88b4adf10490f1edd6f881... | [
"data/p3BR/R2/benchmark/startQiskit289.py",
"benchmark/startQiskit2375.py",
"data/p3BR/R1/benchmark/startQiskit_QC456.py",
"benchmark/startQiskit_Class2296.py",
"data/p4VQE/R4/benchmark/startQiskit_noisy62.py",
"benchmark/startQiskit_noisy2042.py",
"benchmark/startQiskit2925.py",
"benchmark/startQiski... | [
"# qubit number=3\n# total number=60\n\nimport numpy as np\n\nfrom qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ\nfrom qiskit.visualization import plot_histogram\nfrom typing import *\nfrom pprint import pprint\nfrom math import log2\nfrom collections impo... | [
[
"numpy.binary_repr"
],
[
"numpy.binary_repr"
],
[
"numpy.binary_repr"
],
[
"numpy.binary_repr"
],
[
"numpy.amax",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"numpy.meshgrid"
],
[
"numpy.binary_repr"
],
[
"numpy.binary_repr"
],
[
"numpy.bin... |
JoZimmer/ParOptBeam | [
"50d15d8d822a2718f2932807e06c4a7e02f866a3"
] | [
"test_scripts/test_element_and_compare_to_kratos.py"
] | [
"from source.element.cr_beam_element import CRBeamElement\n\nimport numpy as np\n\nnp.set_printoptions(suppress=False, precision=4, linewidth=100)\n\n\ndef test_crbeam_element_update_incremental():\n material_params = {'rho': 7850, 'e': 2069000000.0, 'nu': 0.29, 'zeta': 0.05, 'lx_i': 1.2, 'is_nonlinear': True}\n... | [
[
"numpy.set_printoptions",
"numpy.array"
]
] |
jab0707/UncertainSCI | [
"569c978c4f67dd7bb37e730276f2a376b8639235"
] | [
"tests/test_laguerre_inv.py"
] | [
"import unittest\n\nimport numpy as np\n\nfrom UncertainSCI.families import LaguerrePolynomials\n\n\nclass IDistTestCase(unittest.TestCase):\n \"\"\"\n Tests for (Laguerre polynomial) inversed induced distributions.\n \"\"\"\n\n def test_idistinv_laguerre(self):\n \"\"\"Evaluation of Laguerre inv... | [
[
"numpy.abs",
"numpy.random.random",
"numpy.linalg.norm",
"numpy.random.rand"
]
] |
alnah005/aggregation-for-caesar | [
"d6bca0a1126e0397315d5773401c71075c33ee2f"
] | [
"panoptes_aggregation/running_reducers/tess_user_reducer.py"
] | [
"'''\nTESS User Reducer\n-----------------\nThis module porvides functions to calculate uesr weights for the TESS project.\nExtracts are from Ceasars `PluckFieldExtractor`.\n'''\nfrom .running_reducer_wrapper import running_reducer_wrapper\nimport numpy as np\n\n\n@running_reducer_wrapper(relevant_reduction=True)\n... | [
[
"numpy.log10",
"numpy.where"
]
] |
StarGazer1995/OpenPCDet | [
"4af33e8badb0c8e68c7c94c71b0ec5667aad2348"
] | [
"pcdet/models/backbones_3d/spconv_unet.py"
] | [
"import torch\nimport torch.nn as nn\nimport spconv\nfrom functools import partial\nfrom .spconv_backbone import post_act_block\nfrom ...utils import common_utils\n\n\nclass SparseBasicBlock(spconv.SparseModule):\n expansion = 1\n\n def __init__(self, inplanes, planes, stride=1, downsample=None, indice_key=No... | [
[
"torch.nn.ReLU",
"torch.cat"
]
] |
ChenLi0830/Clevo-Categorization-Service | [
"44b509786849a6dce610171d86e5da68ad748b4b"
] | [
"temp/train_cnn.py"
] | [
"'''This example demonstrates the use of Convolution1D for text classification.\n'''\n\nfrom __future__ import print_function\n\n\nimport sys\nsys.path.append('/Users/wangwei/anaconda2/envs/python3_keras/lib/python3.6/site-packages')\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\n... | [
[
"numpy.max",
"pandas.DataFrame"
]
] |
MOONJOOYOUNG/AdamP | [
"64a63106a2ac62bcbe90627f2a83ec1b488f3416"
] | [
"adamp/sgdp.py"
] | [
"\"\"\"\nAdamP\nCopyright (c) 2020-present NAVER Corp.\nMIT license\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.optim.optimizer import Optimizer, required\nimport math\n\nclass SGDP(Optimizer):\n def __init__(self, params, lr=required, momentum=0, dampening=0,\n weight_decay=0, nes... | [
[
"torch.zeros_like"
]
] |
antgonza/qtp-diversity | [
"0c2ec84711decf798ea6ffdb3e97dc9582ba4035"
] | [
"qtp_diversity/tests/test_validate.py"
] | [
"# -----------------------------------------------------------------------------\n# Copyright (c) 2014--, The Qiita Development Team.\n#\n# Distributed under the terms of the BSD 3-clause License.\n#\n# The full license is in the file LICENSE, distributed with this software.\n# -------------------------------------... | [
[
"numpy.asarray",
"pandas.Series",
"numpy.random.randint"
]
] |
methane/astropy | [
"1a065d5ce403e226799cfb3d606fda33be0a6c08",
"1a065d5ce403e226799cfb3d606fda33be0a6c08"
] | [
"astropy/coordinates/sky_coordinate.py",
"astropy/io/fits/tests/test_core.py"
] | [
"import re\nimport copy\nimport warnings\nimport operator\n\nimport numpy as np\n\nfrom astropy import _erfa as erfa\nfrom astropy.utils.compat.misc import override__dir__\nfrom astropy import units as u\nfrom astropy.constants import c as speed_of_light\nfrom astropy.utils.data_info import MixinInfo\nfrom astropy.... | [
[
"numpy.logical_not",
"numpy.all",
"numpy.isscalar",
"numpy.array",
"numpy.zeros"
],
[
"numpy.all",
"numpy.arange",
"numpy.zeros"
]
] |
LauraOlivera/gammapy | [
"8aadf0ec524bcf51d0ac5655a04507d5d449e7ed"
] | [
"gammapy/estimators/tests/test_flux_point.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport pytest\nimport numpy as np\nfrom numpy.testing import assert_allclose\nimport astropy.units as u\nfrom astropy.table import Table\nfrom gammapy.catalog.fermi import SourceCatalog3FGL\nfrom gammapy.estimators import FluxPoints\nfrom gammapy.mod... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.exp"
]
] |
MaxxWilson/ASE389Project | [
"13c3c72887e27fbed2eef63c1e27b4a185036a39"
] | [
"pnc/draco3_lb_pnc/draco3_lb_controller.py"
] | [
"import numpy as np\n\nfrom util import util\nfrom config.draco3_lb_config import PnCConfig, WBCConfig\nfrom pnc.wbc.ihwbc.ihwbc import IHWBC\nfrom pnc.wbc.ihwbc.joint_integrator import JointIntegrator\n\n\nclass Draco3LBController(object):\n def __init__(self, tci_container, robot):\n self._tci_container... | [
[
"numpy.dot",
"numpy.linalg.inv",
"numpy.eye",
"numpy.array",
"numpy.zeros"
]
] |
Barry-lab/Publication_TanniDeCothiBarry2021 | [
"425bc0bd9a74b837d912820e9ea1539a111fcb1f",
"425bc0bd9a74b837d912820e9ea1539a111fcb1f",
"425bc0bd9a74b837d912820e9ea1539a111fcb1f"
] | [
"visual_change_analysis/main.py",
"electrophysiology_analysis/barrylab_ephys_analysis/spatial/fields.py",
"electrophysiology_analysis/barrylab_ephys_analysis/lfp/oscillations.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom scipy.stats import pearsonr\nfrom bin_data import bin_data\n\n# import pixel data \nright_z_pixel_change = np.load(\"right_z_pixel_change.npy\")\nleft_z_pixel_change = np.load(\"left_z_pixel_change.npy\")\nfront_z_pixel_change = np.lo... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.title",
"matplotlib.pyplot.clim",
"scipy.stats.pearsonr",
"numpy.vstack",
"matplotlib.pyplot.get_cmap",
"numpy.degrees",
"numpy.append",
"matplotlib.pyplot.subplot",
"numpy.std",
"numpy.diff",
"numpy.mean",
"matplot... |
alik-git/mbrl-lib | [
"b364f8e64ca71ebd18147fe8cdbd3068b74e1f1e",
"b364f8e64ca71ebd18147fe8cdbd3068b74e1f1e"
] | [
"mbrl/examples/main.py",
"mbrl/models/planet_legacy.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\nfrom typing import Iterable\nimport hydra\nimport numpy as np\nimport omegaconf\nimport torch\n\nimport mbrl.algo... | [
[
"torch.manual_seed",
"numpy.random.seed"
],
[
"torch.BoolTensor",
"torch.cat",
"torch.zeros",
"torch.FloatTensor",
"torch.stack",
"numpy.array"
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.