Python numpy.amax方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0):
  """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2.

  For each box in boxlist1, we want its IOA to be more than minoverlap with
  at least one of the boxes in boxlist2. If it does not, we remove it.

  Args:
    boxlist1: BoxList holding N boxes.
    boxlist2: BoxList holding M boxes.
    minoverlap: Minimum required overlap between boxes, to count them as
                overlapping.

  Returns:
    A pruned boxlist with size [N', 4].
  """
  intersection_over_area = ioa(boxlist2, boxlist1)  # [M, N] tensor
  intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
  keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
  keep_inds = np.nonzero(keep_bool)[0]
  new_boxlist1 = gather(boxlist1, keep_inds)
  return new_boxlist1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def OutlierDetection(CMat, s):
    n = np.amax(s)
    _, N = CMat.shape
    OutlierIndx = list()
    FailCnt = 0
    Fail = False

    for i in range(0, N):
        c = CMat[:, i]
        if np.sum(np.isnan(c)) >= 1:
            OutlierIndx.append(i)
            FailCnt += 1
    sc = s.astype(float)
    sc[OutlierIndx] = np.nan
    CMatC = CMat.astype(float)
    CMatC[OutlierIndx, :] = np.nan
    CMatC[:, OutlierIndx] = np.nan
    OutlierIndx = OutlierIndx

    if FailCnt > (N - n):
        CMatC = np.nan
        sc = np.nan
        Fail = True
    return CMatC, sc, OutlierIndx, Fail 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def get_wmin_wmax_tmax_ia_def(self, tol):
    from numpy import log, exp, sqrt, where, amin, amax
    """ 
      This is a default choice of the wmin and wmax parameters for a log grid along 
      imaginary axis. The default choice is based on the eigenvalues. 
    """
    E = self.ksn2e[0,0,:]
    E_fermi = self.fermi_energy
    E_homo = amax(E[where(E<=E_fermi)])
    E_gap  = amin(E[where(E>E_fermi)]) - E_homo  
    E_maxdiff = amax(E) - amin(E)
    d = amin(abs(E_homo-E)[where(abs(E_homo-E)>1e-4)])
    wmin_def = sqrt(tol * (d**3) * (E_gap**3)/(d**2+E_gap**2))
    wmax_def = (E_maxdiff**2/tol)**(0.250)
    tmax_def = -log(tol)/ (E_gap)
    tmin_def = -100*log(1.0-tol)/E_maxdiff
    return wmin_def, wmax_def, tmin_def,tmax_def 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def si_c_check (self, tol = 1e-5):
    """
    This compares np.solve and LinearOpt-lgmres methods for solving linear equation (1-v\chi_{0}) * W_c = v\chi_{0}v
    """
    import time
    import numpy as np
    ww = 1j*self.ww_ia
    t = time.time()
    si0_1 = self.si_c(ww)      #method 1:  numpy.linalg.solve
    t1 = time.time() - t
    print('numpy: {} sec'.format(t1))
    t2 = time.time()
    si0_2 = self.si_c2(ww)     #method 2:  scipy.sparse.linalg.lgmres
    t3 = time.time() - t2
    print('lgmres: {} sec'.format(t3))
    summ = abs(si0_1 + si0_2).sum()
    diff = abs(si0_1 - si0_2).sum() 
    if diff/summ < tol and diff/si0_1.size < tol:
       print('OK! scipy.lgmres methods and np.linalg.solve have identical results')
    else:
       print('Results (W_c) are NOT similar!')     
    return [[diff/summ] , [np.amax(abs(diff))] ,[tol]]

  #@profile 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def __init__(self, ao_log, sp):

    self.ion = ao_log.sp2ion[sp]
    self.rr,self.pp,self.nr = ao_log.rr,ao_log.pp,ao_log.nr
    self.interp_rr = log_interp_c(self.rr)
    self.interp_pp = log_interp_c(self.pp)
    
    self.mu2j = ao_log.sp_mu2j[sp]
    self.nmult= len(self.mu2j)
    self.mu2s = ao_log.sp_mu2s[sp]
    self.norbs= self.mu2s[-1]

    self.mu2ff = ao_log.psi_log[sp]
    self.mu2ff_rl = ao_log.psi_log_rl[sp]    
    self.mu2rcut = ao_log.sp_mu2rcut[sp]
    self.rcut = np.amax(self.mu2rcut)
    self.charge = ao_log.sp2charge[sp] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def coords2sort_order(a2c):
  """ Delivers a list of atom indices which generates a near-diagonal overlap for a given set of atom coordinates """
  na  = a2c.shape[0]
  aa2d = squareform(pdist(a2c))
  mxd = np.amax(aa2d)+1.0
  a = 0
  lsa = []
  for ia in range(na):
    lsa.append(a)
    asrt = np.argsort(aa2d[a])
    for ja in range(1,na):
      b = asrt[ja]
      if b not in lsa: break
    aa2d[a,b] = aa2d[b,a] = mxd
    a = b
  return np.array(lsa) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def test_gw(self):
    """ This is GW """
    mol = gto.M(verbose=0, atom='''Ag 0 0 -0.3707; Ag 0 0 0.3707''', basis = 'cc-pvdz-pp',)
    gto_mf = scf.RHF(mol)#.density_fit()
    gto_mf.kernel()
    #print('gto_mf.mo_energy:', gto_mf.mo_energy)
    s = nao(mf=gto_mf, gto=mol, verbosity=0)
    oref = s.overlap_coo().toarray()
    #print('s.norbs:', s.norbs, oref.sum())

    pb = prod_basis(nao=s, algorithm='fp')
    pab2v = pb.get_ac_vertex_array()
    mom0,mom1=pb.comp_moments()
    orec = np.einsum('p,pab->ab', mom0, pab2v)
    self.assertTrue(np.allclose(orec,oref, atol=1e-3), \
      "{} {}".format(abs(orec-oref).sum()/oref.size, np.amax(abs(orec-oref)))) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def spatial_heatmap(array, path, title=None, color="Greens", figformat="png"):
    """Taking channel information and creating post run channel activity plots."""
    logging.info("Nanoplotter: Creating heatmap of reads per channel using {} reads."
                 .format(array.size))
    activity_map = Plot(
        path=path + "." + figformat,
        title="Number of reads generated per channel")
    layout = make_layout(maxval=np.amax(array))
    valueCounts = pd.value_counts(pd.Series(array))
    for entry in valueCounts.keys():
        layout.template[np.where(layout.structure == entry)] = valueCounts[entry]
    plt.figure()
    ax = sns.heatmap(
        data=pd.DataFrame(layout.template, index=layout.yticks, columns=layout.xticks),
        xticklabels="auto",
        yticklabels="auto",
        square=True,
        cbar_kws={"orientation": "horizontal"},
        cmap=color,
        linewidths=0.20)
    ax.set_title(title or activity_map.title)
    activity_map.fig = ax.get_figure()
    activity_map.save(format=figformat)
    plt.close("all")
    return [activity_map] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def optimally_reblocked(data):
    """
        Find optimal reblocking of input data. Takes in pandas
        DataFrame of raw data to reblock, returns DataFrame
        of reblocked data.
    """
    opt = opt_block(data)
    n_reblock = int(np.amax(opt))
    rb_data = reblock_by2(data, n_reblock)
    serr = rb_data.sem(axis=0)
    d = {
        "mean": rb_data.mean(axis=0),
        "standard error": serr,
        "standard error error": serr / np.sqrt(2 * (len(rb_data) - 1)),
        "reblocks": n_reblock,
    }
    return pd.DataFrame(d) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0):
  """Prunes the boxes in list1 that overlap less than thresh with list2.

  For each mask in box_mask_list1, we want its IOA to be more than minoverlap
  with at least one of the masks in box_mask_list2. If it does not, we remove
  it. If the masks are not full size image, we do the pruning based on boxes.

  Args:
    box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks.
    box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks.
    minoverlap: Minimum required overlap between boxes, to count them as
                overlapping.

  Returns:
    A pruned box_mask_list with size [N', 4].
  """
  intersection_over_area = ioa(box_mask_list2, box_mask_list1)  # [M, N] tensor
  intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
  keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
  keep_inds = np.nonzero(keep_bool)[0]
  new_box_mask_list1 = gather(box_mask_list1, keep_inds)
  return new_box_mask_list1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def train(self, batch_size=32):
            # Train using replay experience
            minibatch = random.sample(self.memory, batch_size)
            for memory in minibatch:
                state, action, reward, state_next, done = memory
                # Build Q target:
                # -> Qtarget[!action] = Q[!action]
                #    Qtarget[action] = reward + gamma * max[a'](Q_next(state_next))
                Qtarget = self.model.predict(state)
                dQ = reward
                if not done:
                    dQ += self.gamma * np.amax(self.model.predict(state_next)[0])
                Qtarget[0][action] = dQ
                self.model.fit(state, Qtarget, epochs=1, verbose=0)

            # Decary exploration after training
            if self.epsilon > self.epsilon_min:
                self.epsilon *= self.epsilon_decay 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def guess_normal(universe, group):
    """
    Guess the normal of a liquid slab

    """
    universe.atoms.pack_into_box()
    dim = universe.coord.dimensions

    delta = []
    for direction in range(0, 3):
        histo, _ = np.histogram(
            group.positions[:, direction],
            bins=5,
            range=(0, dim[direction]),
            density=True)
        max_val = np.amax(histo)
        min_val = np.amin(histo)
        delta.append(np.sqrt((max_val - min_val)**2))

    if np.max(delta) / np.min(delta) < 5.0:
        print("Warning: the result of the automatic normal detection (",
              np.argmax(delta), ") is not reliable")
    return np.argmax(delta) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def input_fn(df):
    """Format the downloaded data."""
    # Creates a dictionary mapping from each continuous feature column name (k)
    # to the values of that column stored in a constant Tensor.
    continuous_cols = [df[k].values for k in CONTINUOUS_COLUMNS]
    X_con = np.stack(continuous_cols).astype(np.float32).T

    # Standardise
    X_con -= X_con.mean(axis=0)
    X_con /= X_con.std(axis=0)

    # Creates a dictionary mapping from each categorical feature column name
    categ_cols = [np.where(pd.get_dummies(df[k]).values)[1][:, np.newaxis]
                  for k in CATEGORICAL_COLUMNS]
    n_values = [np.amax(c) + 1 for c in categ_cols]
    X_cat = np.concatenate(categ_cols, axis=1).astype(np.int32)

    # Converts the label column into a constant Tensor.
    label = df[LABEL_COLUMN].values[:, np.newaxis]

    # Returns the feature columns and the label.
    return X_con, X_cat, n_values, label 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def refine_room_region(cw_mask, rm_ind):
	label_rm, num_label = ndimage.label((1-cw_mask))
	new_rm_ind = np.zeros(rm_ind.shape)
	for j in xrange(1, num_label+1):  
		mask = (label_rm == j).astype(np.uint8)
		ys, xs = np.where(mask!=0)
		area = (np.amax(xs)-np.amin(xs))*(np.amax(ys)-np.amin(ys))
		if area < 100:
			continue
		else:
			room_types, type_counts = np.unique(mask*rm_ind, return_counts=True)
			if len(room_types) > 1:
				room_types = room_types[1:] # ignore background type which is zero
				type_counts = type_counts[1:] # ignore background count
			new_rm_ind += mask*room_types[np.argmax(type_counts)]

	return new_rm_ind 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import amax [as 別名]
def get_heatmap(self, target_size):
        heatmap = np.zeros((CocoMetadata.__coco_parts, self.height, self.width), dtype=np.float32)

        for joints in self.joint_list:
            for idx, point in enumerate(joints):
                if point[0] < 0 or point[1] < 0:
                    continue
                CocoMetadata.put_heatmap(heatmap, idx, point, self.sigma)

        heatmap = heatmap.transpose((1, 2, 0))

        # background
        heatmap[:, :, -1] = np.clip(1 - np.amax(heatmap, axis=2), 0.0, 1.0)

        if target_size:
            heatmap = cv2.resize(heatmap, target_size, interpolation=cv2.INTER_AREA)

        return heatmap.astype(np.float16)