def test_who_with_0dim_array(self):
# ticket #1243
import os
import sys
oldstdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
try:
try:
np.who({'foo': np.array(1)})
except Exception:
raise AssertionError("ticket #1243")
finally:
sys.stdout.close()
sys.stdout = oldstdout
def test_who_with_0dim_array(self):
# ticket #1243
import os
import sys
oldstdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
try:
try:
np.who({'foo': np.array(1)})
except Exception:
raise AssertionError("ticket #1243")
finally:
sys.stdout.close()
sys.stdout = oldstdout
def main():
A = Z = 1
R0 = 900
# Build grid
rg = np.linspace(100, 150, 18)
qq = 1.3 + 0 * np.linspace(0, 1, rg.size)**2
theta = np.linspace(-np.pi, np.pi, 41)
vpar = np.multiply.outer(np.linspace(.1, 4, 12), [1, -1])
mu = np.linspace(0, 1, 8)
grid = Grid(1, 1, 900, rg, qq, theta, vpar, mu)
np.who(grid.__dict__)
plt.figure()
plt.plot(grid.radius.squeeze(), grid.psi.squeeze())
plt.show()
def test_who_with_0dim_array(self, level=rlevel) :
"""ticket #1243"""
import os, sys
sys.stdout = open(os.devnull, 'w')
try :
tmp = np.who({'foo' : np.array(1)})
sys.stdout = sys.__stdout__
except :
sys.stdout = sys.__stdout__
raise AssertionError("ticket #1243")
def test_who_with_0dim_array(self, level=rlevel):
"""ticket #1243"""
import os, sys
sys.stdout = open(os.devnull, 'w')
try:
tmp = np.who({'foo': np.array(1)})
sys.stdout = sys.__stdout__
except:
sys.stdout = sys.__stdout__
raise AssertionError("ticket #1243")
def test_who_with_0dim_array(self, level=rlevel):
"""ticket #1243"""
import os, sys
oldstdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
try:
try:
tmp = np.who({'foo': np.array(1)})
except:
raise AssertionError("ticket #1243")
finally:
sys.stdout.close()
sys.stdout = oldstdout
def test_who_with_0dim_array(self, level=rlevel) :
"""ticket #1243"""
import os, sys
oldstdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
try:
try:
tmp = np.who({'foo' : np.array(1)})
except:
raise AssertionError("ticket #1243")
finally:
sys.stdout.close()
sys.stdout = oldstdout
from io import StringIO
from typing import Any, Dict
import numpy as np
AR: np.ndarray[Any, np.dtype[np.float64]]
AR_DICT: Dict[str, np.ndarray[Any, np.dtype[np.float64]]]
FILE: StringIO
def func(a: int) -> bool: ...
reveal_type(np.deprecate(func)) # E: def (a: builtins.int) -> builtins.bool
reveal_type(np.deprecate()) # E: _Deprecate
reveal_type(np.deprecate_with_doc("test")) # E: _Deprecate
reveal_type(np.deprecate_with_doc(None)) # E: _Deprecate
reveal_type(np.byte_bounds(AR)) # E: Tuple[builtins.int, builtins.int]
reveal_type(np.byte_bounds(np.float64())) # E: Tuple[builtins.int, builtins.int]
reveal_type(np.who(None)) # E: None
reveal_type(np.who(AR_DICT)) # E: None
reveal_type(np.info(1, output=FILE)) # E: None
reveal_type(np.source(np.interp, output=FILE)) # E: None
reveal_type(np.lookfor("binary representation", output=FILE)) # E: None
reveal_type(np.safe_eval("1 + 1")) # E: Any
def main():
from grid import Grid
# General parameters
A = Z = 1
R0 = 900
# Build grid
rg = np.linspace(100, 150, 18)
qq = 1.3 + 0*np.linspace(0, 1, rg.size)**2
theta = np.linspace(- np.pi, np.pi, 141)
vpar = np.multiply.outer(np.linspace(.1, 4, 12), [1, -1])
mu = np.linspace(0, 1, 8)
grid = Grid(A, Z, R0, rg, qq, theta, vpar, mu)
np.who(grid.__dict__)
# Advect particles
pot = np.zeros_like(rg)
adv = ParticleAdvector(grid, pot)
adv.compute_invariants()
np.who(adv.__dict__)
adv.compute_bounce_point()
np.who(adv.__dict__)
plt.figure()
plt.suptitle('Banana tip')
plt.subplot(121)
plt.gca().set_title('psi')
plt.plot(adv._banana_psi[:, 0])
plt.xlabel('trapped index')
plt.subplot(122)
plt.gca().set_title('theta')
plt.plot(adv._banana_theta[:, 0])
plt.xlabel('trapped index')
plt.show()
adv.compute_trajectory()
np.who(adv.__dict__)
plt.figure()
plt.suptitle('Trajectory')
plt.subplot(121)
plt.gca().set_title('psi')
plt.plot(theta, adv.psi_path[:, 3].reshape(theta.size, -1))
plt.subplot(122)
plt.gca().set_title('vpar')
plt.plot(theta, adv.vpar_path[:, 3].reshape(theta.size, -1))
plt.show()
# Compute trajectory timing
adv.compute_displacement()
adv.compute_precession()
adv.compute_ballooning()
np.who(adv.__dict__)
living = np.where(
adv.living_path(theta)[..., np.newaxis],
1, np.nan
)
time = adv.time_path * living
phi = adv._trans * living
plt.figure()
plt.suptitle('Displacement')
plt.subplot(121)
plt.gca().set_title('time')
plt.plot(
theta,
time[:, 16].reshape(theta.size, -1),
)
plt.subplot(122)
plt.gca().set_title('phi')
plt.plot(
theta,
phi[:, 16].reshape(theta.size, -1),
)
plt.show()
adv.compute_canon()
np.who(adv.__dict__)
idx = np.argsort(vpar.T.ravel())
plt.figure()
plt.subplot(121)
plt.plot(
vpar.T.ravel()[idx],
adv._banana_momentum.T.reshape(vpar.size, -1)[idx],
)
plt.subplot(122)
plt.plot(
vpar.T.ravel()[idx],
(
adv._banana_momentum
+ grid.Z * (
grid.qprofile * adv._ltor
+ rg[0]**2/2
)
).T.reshape(vpar.size, -1)[idx],
)
plt.show()
import numpy as np np.deprecate(1) # E: No overload variant np.deprecate_with_doc(1) # E: incompatible type np.byte_bounds(1) # E: incompatible type np.who(1) # E: incompatible type np.lookfor(None) # E: incompatible type np.safe_eval(None) # E: incompatible type
# -*- coding: utf-8 -*- """ Created on Fri Oct 11 12:59:39 2019 @author: Enes """ #dir(np) --> np kütüphanesinin fonksiyonlarını gösterir. import numpy as np a = np.arange(10) b = np.ones(20) np.who() #np.tile([1,2],[3,4],3) --->Yan yana üç kere matrisleri kopyalar. 3,2 olsaydı 3 aşağıya 2 sağa kopyalar.. #np.triu(x) --> Matrisi üst üçgen yapar. #np.tril(x) --> Matrisi alt üçgen yapar. #np.var(x) --> Varyansını alır. #np.var(x,axix=0) --> Her sütunun ayrı ayrı varyansını alır. axix=1 ise de satırları yapar. #sum --> toplama demek. #np.sum(x) --> toplar. #np.cumsum --> kümülatif toplama yapar. Verileri toplaya toplaya gider. #pn.prod(x,axix=0) --> x değerlerinin düşey yönde çarpımlarını bulur. axix=1 de yatayda yapar. #pn.cumprod(x,axix=0) #np.hstack((a,b)) --> Yatay yönde a ve b matrislerini yan yana koyar. #np.hstack((a,b)) --> Bu da dikey yönde yapar. #np.stack((a,b),axix=0) --> Böyle kullaırsak da birleştirme yapmaz. Direkt boyut arttırır. #-------------------# #***Önemli, sınavda çıkabilir.*** #r=np.array([2,3,5])
