python类ceil()的实例源码

def _nearest_pow_2(x):
    """
    Find power of two nearest to x
    >>> _nearest_pow_2(3)
    2.0
    >>> _nearest_pow_2(15)
    16.0
    :type x: float
    :param x: Number
    :rtype: Int
    :return: Nearest power of 2 to x
    """
    a = M.pow(2, M.ceil(np.log2(x)))
    b = M.pow(2, M.floor(np.log2(x)))
    if abs(a - x) < abs(b - x):
        return a
    else:
        return b

def test_partial_send(self, *args):
        c = self.make_connection()

        # only write the first four bytes of the OptionsMessage
        write_size = 4
        c._socket.send.side_effect = None
        c._socket.send.return_value = write_size
        c.handle_write(None, 0)

        msg_size = 9  # v3+ frame header
        expected_writes = int(math.ceil(float(msg_size) / write_size))
        size_mod = msg_size % write_size
        last_write_size = size_mod if size_mod else write_size
        self.assertFalse(c.is_defunct)
        self.assertEqual(expected_writes, c._socket.send.call_count)
        self.assertEqual(last_write_size, len(c._socket.send.call_args[0][0]))

def test_partial_send(self, *args):
        c = self.make_connection()

        # only write the first four bytes of the OptionsMessage
        write_size = 4
        c.socket.send.side_effect = None
        c.socket.send.return_value = write_size
        c.handle_write()

        msg_size = 9  # v3+ frame header
        expected_writes = int(math.ceil(float(msg_size) / write_size))
        size_mod = msg_size % write_size
        last_write_size = size_mod if size_mod else write_size
        self.assertFalse(c.is_defunct)
        self.assertEqual(expected_writes, c.socket.send.call_count)
        self.assertEqual(last_write_size, len(c.socket.send.call_args[0][0]))

def compute_logarithmic_scale(min_, max_, min_scale, max_scale):
    """Compute an optimal scale for logarithmic"""
    if max_ <= 0 or min_ <= 0:
        return []
    min_order = int(floor(log10(min_)))
    max_order = int(ceil(log10(max_)))
    positions = []
    amplitude = max_order - min_order
    if amplitude <= 1:
        return []
    detail = 10.
    while amplitude * detail < min_scale * 5:
        detail *= 2
    while amplitude * detail > max_scale * 3:
        detail /= 2
    for order in range(min_order, max_order + 1):
        for i in range(int(detail)):
            tick = (10 * i / detail or 1) * 10 ** order
            tick = round_to_scale(tick, tick)
            if min_ <= tick <= max_ and tick not in positions:
                positions.append(tick)
    return positions

def Occlusion_exp(image,occluding_size,occluding_stride,model,preprocess,classes,groundTruth):    
    img = np.copy(image)
    height, width,_= img.shape
    output_height = int(math.ceil((height-occluding_size)/occluding_stride+1))
    output_width = int(math.ceil((width-occluding_size)/occluding_stride+1))
    ocludedImages=[]
    for h in range(output_height):
        for w in range(output_width):
            #occluder region
            h_start = h*occluding_stride
            w_start = w*occluding_stride
            h_end = min(height, h_start + occluding_size)
            w_end = min(width, w_start + occluding_size)

            input_image = copy.copy(img)
            input_image[h_start:h_end,w_start:w_end,:] =  0
            ocludedImages.append(preprocess(Image.fromarray(input_image)))

    L = np.empty(output_height*output_width)
    L.fill(groundTruth)
    L = torch.from_numpy(L)
    tensor_images = torch.stack([img for img in ocludedImages])
    dataset = torch.utils.data.TensorDataset(tensor_images,L) 
    dataloader = torch.utils.data.DataLoader(dataset,batch_size=5,shuffle=False, num_workers=8) 

    heatmap=np.empty(0)
    model.eval()
    for data in dataloader:
        images, labels = data

        if use_gpu:
            images, labels = (images.cuda()), (labels.cuda(async=True))

        outputs = model(Variable(images))
        m = nn.Softmax()
        outputs=m(outputs)
        if use_gpu:   
            outs=outputs.cpu()
        heatmap = np.concatenate((heatmap,outs[0:outs.size()[0],groundTruth].data.numpy()))

    return heatmap.reshape((output_height, output_width))

def percentile(N, percent, key=lambda x:x):
    """
    Find the percentile of a list of values.

    @parameter N - is a list of values. Note N MUST BE already sorted.
    @parameter percent - a float value from 0.0 to 1.0.
    @parameter key - optional key function to compute value from each element of N.

    @return - the percentile of the values
    """
    if not N:
        return None
    k = (len(N)-1) * percent
    f = math.floor(k)
    c = math.ceil(k)
    if f == c:
        return key(N[int(k)])
    d0 = key(N[int(f)]) * (c-k)
    d1 = key(N[int(c)]) * (k-f)
    return d0+d1

# median is 50th percentile.

def gen_sieve(ceiling=None):
    if ceiling is not None:
        if ceiling % 2 == 0:
            ceiling -= 1
        highest_prime = math.ceil(math.sqrt(ceiling))
    last_val = 1
    found_primes = []
    yield 2
    while ceiling is None or ceiling > last_val:
        current_val = None
        while current_val is None:
            current_val = last_val = last_val + 2
            for prime, square in found_primes:
                if current_val < square: 
                    break
                if current_val % prime == 0:
                    current_val = None
                    break
        yield current_val
        if ceiling is None or highest_prime > last_val:
            found_primes.append((current_val, current_val ** 2))

def stop_foraging(self):
        if self.last_action and self.last_action[0] == "foraging":
            item = self.last_action[1]
            time_spent = math.ceil(time.time() - self.last_action_time) / 60
            self.last_action = None
            self.last_action_time = None
            item_amount = math.floor(time_spent * self.foraging_rate)
            self.inventory[item] = self.inventory.get(item, 0) + item_amount
            if self.inventory[item] == 0:
                del self.inventory[item]
            self.foraging_xp += item_amount
            secondary_item = forageables[item][0]
            tertiary_item = forageables[item][1]
            secondary_amount = random.randint(0, item_amount)
            tertiary_amount = math.floor(random.randint(0, item_amount) / 100)
            self.inventory[secondary_item] = self.inventory.get(secondary_item, 0) + secondary_amount
            if self.inventory[secondary_item] == 0:
                del self.inventory[secondary_item]
            self.inventory[tertiary_item] = self.inventory.get(tertiary_item, 0) + tertiary_amount
            if self.inventory[tertiary_item] == 0:
                del self.inventory[tertiary_item]
            self.write_data()
            return item, time_spent, item_amount, secondary_amount, tertiary_amount
        else:
            return False, self.last_action

def stop_woodcutting(self):
        if self.last_action and self.last_action[0] == "woodcutting":
            wood_type = self.last_action[1]
            time_spent = math.ceil(time.time() - self.last_action_time) / 60
            self.last_action = None
            self.last_action = None
            current_wood_lvl = wood_lvl(wood_type)
            wood_amount = math.floor(time_spent * self.wood_rate(wood_type) * self.woodcutting_rate)
            xp_amount = current_wood_lvl * wood_amount
            self.inventory[wood_type] = self.inventory.get(wood_type, 0) + wood_amount
            if self.inventory[wood_type] == 0:
                del self.inventory[wood_type]
            self.woodcutting_xp += xp_amount
            self.write_data()
            return wood_type, time_spent, wood_amount, xp_amount
        else:
            return False, self.last_action

def download_mutation_images(self):
        # TODO: dunno if this works
        import ipywidgets
        import math

        views = []
        for g in self.reference_gempro.genes:
            if g.protein.representative_structure:
                view = g.protein.view_all_mutations(alignment_type='seqalign', grouped=False, structure_opacity=0.5,
                                                    opacity_range=(0.6, 1), scale_range=(.5, 5))
                view._remote_call("setSize", target='Widget', args=['300px', '300px'])
                view.download_image(filename='{}_{}_mutations.png'.format(g.id, g.name))
                views.append(view)

        hboxes = [ipywidgets.HBox(views[i * 3:i * 3 + 3])
                  for i in range(int(math.ceil(len(views) / 3.0)))]
        vbox = ipywidgets.VBox(hboxes)
        return vbox

def __call__(self):
        total_processes = _calculate_workers()
        ctxt = {
            "service_name": self.service_name,
            "user": self.user,
            "group": self.group,
            "script": self.script,
            "admin_script": self.admin_script,
            "public_script": self.public_script,
            "processes": int(math.ceil(self.process_weight * total_processes)),
            "admin_processes": int(math.ceil(self.admin_process_weight *
                                             total_processes)),
            "public_processes": int(math.ceil(self.public_process_weight *
                                              total_processes)),
            "threads": 1,
            "usr_bin": git_determine_usr_bin(),
            "python_path": git_determine_python_path(),
        }
        return ctxt

def __call__(self):
        total_processes = _calculate_workers()
        ctxt = {
            "service_name": self.service_name,
            "user": self.user,
            "group": self.group,
            "script": self.script,
            "admin_script": self.admin_script,
            "public_script": self.public_script,
            "processes": int(math.ceil(self.process_weight * total_processes)),
            "admin_processes": int(math.ceil(self.admin_process_weight *
                                             total_processes)),
            "public_processes": int(math.ceil(self.public_process_weight *
                                              total_processes)),
            "threads": 1,
            "usr_bin": git_determine_usr_bin(),
            "python_path": git_determine_python_path(),
        }
        return ctxt

def __call__(self):
        total_processes = _calculate_workers()
        ctxt = {
            "service_name": self.service_name,
            "user": self.user,
            "group": self.group,
            "script": self.script,
            "admin_script": self.admin_script,
            "public_script": self.public_script,
            "processes": int(math.ceil(self.process_weight * total_processes)),
            "admin_processes": int(math.ceil(self.admin_process_weight *
                                             total_processes)),
            "public_processes": int(math.ceil(self.public_process_weight *
                                              total_processes)),
            "threads": 1,
            "usr_bin": git_determine_usr_bin(),
            "python_path": git_determine_python_path(),
        }
        return ctxt

def __call__(self):
        total_processes = _calculate_workers()
        ctxt = {
            "service_name": self.service_name,
            "user": self.user,
            "group": self.group,
            "script": self.script,
            "admin_script": self.admin_script,
            "public_script": self.public_script,
            "processes": int(math.ceil(self.process_weight * total_processes)),
            "admin_processes": int(math.ceil(self.admin_process_weight *
                                             total_processes)),
            "public_processes": int(math.ceil(self.public_process_weight *
                                              total_processes)),
            "threads": 1,
            "usr_bin": git_determine_usr_bin(),
            "python_path": git_determine_python_path(),
        }
        return ctxt

def __call__(self):
        total_processes = _calculate_workers()
        ctxt = {
            "service_name": self.service_name,
            "user": self.user,
            "group": self.group,
            "script": self.script,
            "admin_script": self.admin_script,
            "public_script": self.public_script,
            "processes": int(math.ceil(self.process_weight * total_processes)),
            "admin_processes": int(math.ceil(self.admin_process_weight *
                                             total_processes)),
            "public_processes": int(math.ceil(self.public_process_weight *
                                              total_processes)),
            "threads": 1,
            "usr_bin": git_determine_usr_bin(),
            "python_path": git_determine_python_path(),
        }
        return ctxt

def save_ims(filename, ims, dpi=100, scale=0.5):
    n, c, h, w = ims.shape

    rows = int(math.ceil(math.sqrt(n)))
    cols = int(round(math.sqrt(n)))

    fig, axes = plt.subplots(rows, cols, figsize=(w*cols/dpi*scale, h*rows/dpi*scale), dpi=dpi)

    for i, ax in enumerate(axes.flat):
        if i < n:
            ax.imshow(ims[i].transpose((1, 2, 0)))
        ax.set_xticks([])
        ax.set_yticks([])
        ax.axis('off')

    plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0.1, hspace=0.1)
    plt.savefig(filename, dpi=dpi, bbox_inces='tight', transparent=True)
    plt.clf()
    plt.close()

def __init__(self, archivelist):
        self.node_archives = archivelist
        self.jobdir = os.path.dirname(archivelist[0])
        self.job_id = "1"
        self.end_str = "end"
        self.walltime = 9751
        self.nodecount = len(archivelist)
        self.acct = {"end_time": 12312, "id": 1, "uid": "sdf", "user": "werqw"}
        self.nodes = ["node" + str(i) for i in xrange(len(archivelist))]
        self._data = {}

        archive_starts = []
        archive_ends = []
        for archive in archivelist:
            context = pmapi.pmContext(c_pmapi.PM_CONTEXT_ARCHIVE, archive)
            mdata = context.pmGetArchiveLabel()
            archive_starts.append(datetime.datetime.utcfromtimestamp(math.floor(mdata.start)))
            archive_ends.append(datetime.datetime.utcfromtimestamp(math.ceil(context.pmGetArchiveEnd())))

        self.start_datetime = min(archive_starts)
        self.end_datetime = max(archive_ends)

def adjust_job_start_end(job):
    """ Set the job node start and end times based on the presence of the special
     job-X-begin and job-X-end archives. Do nothing if these archives are absent
    """

    startarchive = "job-{0}-begin".format(job.job_id)
    endarchive = "job-{0}-end".format(job.job_id)

    for nodename, filepaths in job.rawarchives():
        begin = None
        end = None
        for fname in filepaths:
            filename = os.path.basename(fname)
            if filename.startswith(startarchive):
                context = pmapi.pmContext(c_pmapi.PM_CONTEXT_ARCHIVE, fname)
                mdata = context.pmGetArchiveLabel()
                begin = datetime.datetime.utcfromtimestamp(math.floor(mdata.start))

            if filename.startswith(endarchive):
                context = pmapi.pmContext(c_pmapi.PM_CONTEXT_ARCHIVE, fname)
                end = datetime.datetime.utcfromtimestamp(math.ceil(context.pmGetArchiveEnd()))

        job.setnodebeginend(nodename, begin, end)

def run_if_needed(base_file, then_file, now_file):
    # Python uses doubles for mtime so it can't precisely represent linux's
    # nanosecond precision. Round up to next whole second to ensure we get a
    # stable timestamp that is guaranteed to be >= the timestamp of the
    # compiler. This also avoids issues if the compiler is on a file system
    # with high-precision timestamps, but the build directory isn't.
    base_stat = os.stat(base_file)
    mtime = math.ceil(base_stat.st_mtime)
    atime = math.ceil(base_stat.st_atime)

    if (os.path.exists(then_file)
            and os.path.exists(now_file)
            and os.stat(then_file).st_mtime == mtime):
        return # Don't need to do anything.

    createIfNeeded(now_file)
    os.utime(now_file, None) # None means now

    createIfNeeded(then_file)
    os.utime(then_file, (atime, mtime))

def col_profile(num_cols, height):
    profile = np.zeros(num_cols)
    peak_width = int(math.ceil(num_cols * 0.125))

    # average number of pixels should be height
    for i in range(0, peak_width):
        profile[i] = height
    # average number of pixels should be 10% of height
    for i in range(peak_width, num_cols - peak_width):
        profile[i] = height * .1
    # average number of pixels should be height
    for i in range(num_cols - peak_width, num_cols):
        profile[i] = height

    # normalize to between 0 and 1
    profile *= 1.0 / profile.max()
    return profile