Python time.time() Examples

def test_point_projects_to_edge(self):
        # p = (114.83299055, 26.8892277)
        p = (121.428387, 31.027371)
        a = time.time()
        edges, segments = self.sg.point_projects_to_edges(p, 0.01)
        print(time.time() - a)

        if self.show_plots:
            plt.figure()
            s2g.plot_lines(MultiLineString(segments), color='orange')  # original roads
            for i in range(0, len(edges)):
                s, e = edges[i]
                sxy = self.sg.node_xy[s]
                exy = self.sg.node_xy[e]
                plt.plot([sxy[0], exy[0]], [sxy[1], exy[1]], color='green')  # graph edges
            plt.plot(p[0], p[1], color='red', markersize=12, marker='o')  # bridges
            plt.show() 

def read_snapshot_dir(path: Path):
    db_path = path / 'snapshot.sql3'
    assert os.path.exists(db_path), f'no {db_path}, use rpm_repo_snapshot()'
    location_to_obj = add_snapshot_db_objs(sqlite3.connect(db_path))
    for repo in os.listdir(path / 'repos'):
        # Make JSON metadata for the repo's GPG keys.
        key_dir = path / 'repos' / repo / 'gpg_keys'
        for key_filename in os.listdir(key_dir.decode()):
            with open(key_dir / key_filename, 'rb') as infile:
                key_content = infile.read()
            location_to_obj[os.path.join(repo.decode(), key_filename)] = {
                'size': len(key_content),
                # We don't have a good timestamp for these, so set it to
                # "now".  Caching efficiency losses should be negligible :)
                'build_timestamp': int(time.time()),
                'content_bytes': key_content,  # Instead of `storage_id`
            }
    return location_to_obj 

def utilize_session(self, model_name, data_provider, **kwargs):
        with SessionHandler(model=self.model, model_name=model_name) as session_handler:
            session_handler.load_weights_or_init()
            start_time = time.time()
            step = session_handler.step
            available = locals()
            available.pop("self", None)
            available.update(kwargs)
            print(f"{'Resuming' if step > 0 else 'Starting'} {model_name}: at step {step}")

            for input_feature in data_provider:
                available["input_feature"] = input_feature
                for hook in self.hooks:
                    provided = hook(self=self, **available)
                    available.update(provided)

                    yield hook.get_yieldables(self=self, **available) 

def set(self):
        """Invoked by the camera thread when a new frame is available."""
        now = time.time()
        remove = None
        for ident, event in self.events.items():
            if not event[0].isSet():
                # if this client's event is not set, then set it
                # also update the last set timestamp to now
                event[0].set()
                event[1] = now
            else:
                # if the client's event is already set, it means the client
                # did not process a previous frame
                # if the event stays set for more than 5 seconds, then assume
                # the client is gone and remove it
                if now - event[1] > 5:
                    remove = ident
        if remove:
            del self.events[remove] 

def _thread(cls):
        """Camera background thread."""
        print('Starting camera thread.')
        frames_iterator = cls.frames()
        for frame in frames_iterator:
            BaseCamera.frame = frame
            BaseCamera.event.set()  # send signal to clients
            time.sleep(0)

            # if there hasn't been any clients asking for frames in
            # the last 10 seconds then stop the thread
            if time.time() - BaseCamera.last_access > 10*60*500:
                frames_iterator.close()
                print('Stopping camera thread due to inactivity.')
                break
        BaseCamera.thread = None 

def generate_window_counts(self):
        self._generate_windows()
        print("** Window read counting started for {} libraries...".format(len(
            self._lib_dict)), flush=True)
        t_start = time()
        for lib_name, lib in self._lib_dict.items():
            print(lib_name, flush=True)
            for replicon in self._replicon_dict:
                lib.replicon_dict[replicon][
                    "window_list"] = self._replicon_dict[replicon][
                        "window_list"]
            lib.count_reads_for_windows()
        t_end = time()
        print("Window read counting finished in {} seconds.\n".format(
            t_end-t_start), flush=True)
        print("** Generating data frames and filtering windows...", flush=True)
        t_start = time()
        self._convert_to_data_frame()
        t_end = time()
        print("Data frame generation and filtering finished in {} seconds.\n"
              .format(t_end-t_start), flush=True) 

def _plot_and_write_windows_gfold(self):
        # plot windows
        print("* Plotting normalized windows...", flush=True)
        t_start = time()
        sig_window_df = self._window_df[self._window_df.significant]
        unsig_window_df = self._initial_window_df[
            ~self._initial_window_df.index.isin(sig_window_df.index)]
        self._plot_initial_windows(unsig_window_df.base_means,
                                   unsig_window_df.fold_change,
                                   sig_window_df.base_means,
                                   sig_window_df.fold_change)
        t_end = time()
        print("Plotting took {} seconds.".format(t_end-t_start), flush=True)
        # write windows after prefiltering with test results
        self._window_df.to_csv(
            "{}/windows_after_prefiltering.csv".format(self._output_folder),
            sep='\t', index=False, encoding='utf-8') 

def _filter_peaks_without_control(self, df):
        # calculate mad for original data frame
        median_abs_dev_from_zero = mad(df.loc[:, self._exp_lib_list].mean(
            axis=1), center=0.0)
        # minimum expression cutoff based on mean over experiment libraries
        print("Removing peaks based on mad cutoff from DataFrame "
              "with {} rows...".format(len(df)), flush=True)
        t_start = time()
        min_expr = (self._mad_multiplier * median_abs_dev_from_zero)
        print("Minimal peak expression based on mean over RIP/CLIP "
              "libraries:" "{} (MAD from zero: {})".format(
                  min_expr, median_abs_dev_from_zero), flush=True)
        df = df.loc[df.loc[:, self._exp_lib_list].mean(axis=1) >= min_expr, :]
        t_end = time()
        print("Removal took {} seconds. DataFrame contains now {} rows.".
              format((t_end-t_start), len(df)), flush=True)
        return df 

def path_generate(a):
    pos = {'1': [32, 32],
           '2': [128, 32],
           '3': [32, 128],
           '4': [128, 128]}
    path = []
    t0 = (int(round(time.time() * 1000)))
    t00 = 0
    for j in range(0, 3):
        for i in range(0, 7):
            x = pos[a[j]][0] + i * (pos[a[j + 1]][0] - pos[a[j]][0]) / 6 + int(random.uniform(1, 3))

            y = pos[a[j]][1] + i * (pos[a[j + 1]][1] - pos[a[j]][1]) / 6 + int(random.uniform(2, 3))

            t = 30 * int(random.uniform(1, 2))
            t00 += t

            path0 = [x, y, t00]
            path.append(path0)
    path[0][2] = t0
    # print path
    return path 

def start(self):       
        
        ####################
        start = time.time()
        ####################         

        self._create_folder_structure()
        self._clear_previous_executions()        
        self._add_ipynb()  
        self._get_flow_results()
        self._add_network_context()
        self._add_geo_localization()
        self._add_reputation()        
        self._create_flow_scores()
        self._get_oa_details()
        self._ingest_summary()

        ##################
        end = time.time()
        print(end - start)
        ################## 

def start(self):

        ####################
        start = time.time()
        ####################

        self._create_folder_structure()
        self._clear_previous_executions()   
        self._add_ipynb()
        self._get_proxy_results()
        self._add_reputation() 
        self._add_iana()
        self._add_network_context() 
        self._create_proxy_scores_csv()
        self._get_oa_details()
        self._ingest_summary()


        ##################
        end = time.time()
        print(end - start)
        ################## 

def start(self):

        ####################
        start = time.time()
        ####################

        self._clear_previous_executions()
        self._create_folder_structure()
        self._add_ipynb()
        self._get_dns_results()
        self._add_tld_column()
        self._add_reputation()
        self._add_hh_column()
        self._add_iana()
        self._add_network_context()
        self._create_dns_scores()
        self._get_oa_details()
        self._ingest_summary()

        ##################
        end = time.time()
        print(end - start)
        ################## 

def test_concurrent():
    # run this in multiple processes,
    # the idea is that collisions will crash, but not munge data
    import random
    import time
    if not os.path.exists("tmp_test_con.log"):
        init_data_file("tmp_test_con.log")

    k = LadderData("tmp_test_con.log")
    # test concurrent log writes
    thread = str(random.randint(0, 100))
    print thread
    for x in range(100):
        time.sleep(0.01)
        k.add_player(thread + '_' + str(x))

    p, g = k.get_players_games()
    print len(p) 

def pretty_time(time, now=datetime.datetime.now()):
    tomorrow = now + datetime.timedelta(days=1)
    next_day = now + datetime.timedelta(days=2)
    next_week = now + datetime.timedelta(weeks=1)
    pretty_fmt = '%-I:%M %p'
    pretty_prefix = ''
    if tomorrow < time < next_day:
        pretty_prefix = 'tomorrow at '
    elif time > next_day and time < next_week:
        pretty_prefix = '%A at '
    elif time > next_week and time.month == now.month and time.year == now.year:
        pretty_prefix = '%A the %-d' + pretty_suffix(time.day) + ' at '
    elif time > next_week and time.year == now.year:
        pretty_prefix = '%B %-d' + pretty_suffix(time.day) + ' at '
    elif time > next_week:
        pretty_prefix = '%B %-d' + pretty_suffix(time.day) + ' %Y at '

    return time.strftime(pretty_prefix + pretty_fmt) 

def arg_parser():
    parser = argparse.ArgumentParser(description='A tool for putting websites into a black hole.')
    commands = parser.add_subparsers(title='commands', metavar='COMMAND')
    commands.required = True
    commands.dest = "commands"

    deny = commands.add_parser('deny', description='Add a site to the black hole. It will become unreachable.', help='Add a site to the black hole. It will become unreachable.')
    deny.add_argument('sitename', help='The website to be blackholed. A URL will be stripped down correctly.')
    deny.set_defaults(func=deny_site)

    allow = commands.add_parser('allow', description='Allow access to a blackholed site for a spell.', help='Allow access to a blackholed site for a spell.')
    time_or_tomorrow = allow.add_mutually_exclusive_group()
    time_or_tomorrow.add_argument('-t', '--time', help='sets the duration to enable a site for. Default is five minutes.', default=5, type=int)
    time_or_tomorrow.add_argument('-m', '--morning', help='allow all sites until tomorrow morning at 6am', action='store_true')
    all_or_one = allow.add_mutually_exclusive_group(required=True)
    all_or_one.add_argument('-a', '--all', action='store_true', help='All blackholed hostnames will be granted access instead of a matching sitename.')
    all_or_one.add_argument('sitename', help='All blackholed hostnames that contain this string will be temporarlly granted access.', nargs='?')
    allow.set_defaults(func=allow_site)

    return parser 

def current_milli_time():
    return int(round(time.time() * 1000)) 

def wait_for_message(d):
    client.loop_start()
    time.sleep(d)
    client.loop_stop()

#Για πάντα 

def test_subgraph_within_box(self):
        bounding_box = box(121.428387, 31.027371, 121.430863, 31.030227)
        a = time.time()
        subgraph = self.sg.subgraph_within_box(bounding_box)
        print(time.time() - a)
        if self.show_plots:
            plt.figure()
            nx.draw(subgraph, pos=self.sg.node_xy, node_size=50)
            plt.show() 

def _populate_sendstream_dict(d):
    d['build_start_time'] = _float_to_sec_nsec_tuple(time.time())
    yield d
    d['dump'] = subprocess.run(
        ['btrfs', 'receive', '--dump'],
        input=d['sendstream'], stdout=subprocess.PIPE, check=True,
        # split into lines to make the `pretty` output prettier
    ).stdout.rstrip(b'\n').split(b'\n')
    d['build_end_time'] = _float_to_sec_nsec_tuple(time.time())


# Takes `path_in_repo` because this is part of the library interface, and
# thus must work in @mode/opt, and thus we cannot use `__file__` here. 

def new(cls, *, xml: bytes):  # NamedTuple.__new__ cannot be overridden
        repodatas = frozenset(_parse_repomd(xml))
        return cls.__new__(
            cls,
            xml=xml,
            fetch_timestamp=int(time.time()),
            build_timestamp=max(r.build_timestamp for r in repodatas),
            repodatas=repodatas,
            checksum=Checksum(
                algorithm=CANONICAL_HASH,
                hexdigest=hashlib.new(CANONICAL_HASH, xml).hexdigest(),
            ),
            size=len(xml),
        ) 

def test_retry_fn(self):

        class Retriable:
            def __init__(self, attempts_to_fail=0):
                self.attempts = 0
                self.first_success_attempt = attempts_to_fail + 1

            def run(self):
                self.attempts += 1
                if self.attempts >= self.first_success_attempt:
                    return self.attempts
                raise RuntimeError(self.attempts)

        self.assertEqual(1, retry_fn(
            Retriable().run, delays=[], what='succeeds immediately'
        ))

        # Check log messages, and ensure that delays add up as expected
        start_time = time.time()
        with self.assertLogs(common_log) as log_ctx:
            self.assertEqual(4, retry_fn(
                Retriable(3).run, delays=[0, 0.1, 0.2], what='succeeds on try 4'
            ))
        self.assertTrue(any(
            '\n[Retry 3 of 3] succeeds on try 4 -- waiting 0.2 seconds.\n' in o
                for o in log_ctx.output
        ))
        self.assertGreater(time.time() - start_time, 0.3)

        # Check running out of retries
        with self.assertLogs(common_log) as log_ctx, \
                self.assertRaises(RuntimeError) as ex_ctx:
            retry_fn(Retriable(100).run, delays=[0] * 7, what='never succeeds')
        self.assertTrue(any(
            '\n[Retry 7 of 7] never succeeds -- waiting 0 seconds.\n' in o
                for o in log_ctx.output
        ))
        self.assertEqual((8,), ex_ctx.exception.args) 

def time_diff(start_time):
    m, s = divmod(time.time() - start_time, 60)
    h, m = divmod(m, 60)
    d, h = divmod(h, 24)
    return "%d:%02d:%02d:%02d" % (d, h, m, s) 

def _maybe_save(self, session_handler, step, start_time, save_every_n_steps):
        if step % save_every_n_steps == 0 and step > 0:
            session_handler.save(step=step)
            print(f"Step {step}, time: {time_diff(start_time)}: Saving in {session_handler.checkpoint_dir}") 

def load_all(self):
        """The function to load all data and labels

        Give:
        data: the list of raw data, needs to be decompressed
              (e.g., raw JPEG string)
        labels: numpy array, with each element is a string
        """
        start = time.time()
        print("Start Loading Data from BCF {}".format(
            'MEMORY' if self._bcf_mode == 'MEM' else 'FILE'))

        self._labels = np.loadtxt(self._label_fn).astype(str)

        if self._bcf.size() != self._labels.shape[0]:
            raise Exception("Number of samples in data"
                            "and labels are not equal")
        else:
            for idx in range(self._bcf.size()):
                datum_str = self._bcf.get(idx)
                self._data.append(datum_str)
        end = time.time()
        print("Loading {} samples Done: Time cost {} seconds".format(
            len(self._data), end - start))

        return self._data, self._labels 

def wait(self):
        """Invoked from each client's thread to wait for the next frame."""
        ident = get_ident()
        if ident not in self.events:
            # this is a new client
            # add an entry for it in the self.events dict
            # each entry has two elements, a threading.Event() and a timestamp
            self.events[ident] = [threading.Event(), time.time()]
        return self.events[ident][0].wait() 

def __init__(self):
        """Start the background camera thread if it isn't running yet."""
        if BaseCamera.thread is None:
            BaseCamera.last_access = time.time()

            # start background frame thread
            BaseCamera.thread = threading.Thread(target=self._thread)
            BaseCamera.thread.start()

            # wait until frames are available
            while self.get_frame() is None:
                time.sleep(0) 

def get_frame(self):
        """Return the current camera frame."""
        BaseCamera.last_access = time.time()

        # wait for a signal from the camera thread
        BaseCamera.event.wait()
        BaseCamera.event.clear()

        return BaseCamera.frame 

def _convert_to_data_frame(self):
        self._window_df = pd.DataFrame()
        for replicon in sorted(self._replicon_dict):
            for strand in ["+", "-"]:
                # add window positions to data frame
                row_number = len(self._replicon_dict[replicon]["window_list"])
                df = pd.concat([
                    pd.Series([replicon] * row_number),
                    pd.Series([strand] * row_number),
                    pd.Series([window[0]+1 for window in
                               self._replicon_dict[
                                   replicon]["window_list"]]),
                    pd.Series([window[1] for window in
                               self._replicon_dict[
                        replicon]["window_list"]])], axis=1)
                df.columns = ["replicon", "strand", "w_start", "w_end"]
                # add library counts to data frame
                for lib_name, lib in self._lib_dict.items():
                    df[lib_name] = (pd.Series(lib.replicon_dict[
                        replicon]["window_counts"].loc[:, strand]))
                self._window_df = self._window_df.append(df,
                                                         ignore_index=True)
            del self._replicon_dict[replicon]["window_list"]
        # remove windows without expression in any library
        print("Removing empty windows from DataFrame with {} rows...".format(
            len(self._window_df.index)), flush=True)
        t_start = time()
        self._window_df = self._window_df.loc[
            (self._window_df.loc[:, self._lib_names_list].sum(axis=1) > 0), :]
        t_end = time()
        print("Removal took {} seconds. DataFrame contains now {} rows.".
              format((t_end-t_start), len(self._window_df.index)), flush=True)
        if self._window_df.empty:
            print("**Dataframe empty**", flush=True)
            return
        if self._stat_test == "gtest":
            self._run_gtest_preprocessing()
        elif self._stat_test == "deseq":
            self._run_deseq_preprocessing() 

def _run_gtest_preprocessing(self):
        # define size factors
        self._define_size_factors()
        # add pseudocounts
        self._window_df[self._lib_names_list] += 1.0
        # normalize counts
        self._window_df[self._lib_names_list] = self._window_df[
            self._lib_names_list].div(
                self._size_factors, axis='columns')
        t_end = time()
        # calculate base means for all windows
        print("Calculating base means and fold changes...", flush=True)
        t_start = time()
        self._window_df["base_means"] = self._window_df.loc[
            :, self._lib_names_list].mean(axis=1)
        # calculate fcs for all windows
        self._window_df["fold_change"] = (
            self._window_df.loc[:, self._exp_lib_list].sum(axis=1) /
            self._window_df.loc[:, self._ctr_lib_list].sum(axis=1))
        t_end = time()
        print("Calculation took {} seconds.".format(t_end-t_start), flush=True)
        # write raw windows to file
        print("Writing normalized windows to file...", flush=True)
        t_start = time()
        self._window_df.to_csv("{}/raw_windows.csv".format(
            self._output_folder), sep='\t', index=False, encoding='utf-8')
        t_end = time()
        print("Writing took {} seconds.".format(t_end-t_start), flush=True)
        # filter windows
        print("* Filtering windows...", flush=True)
        self._initial_window_df = self._window_df.copy()
        self._window_df = self._prefilter_windows_gtest(self._window_df) 

def _prefilter_windows_deseq(self, df):
        print("Removing windows where not all experiment libs show "
              "expression from DataFrame with {} rows...".format(len(df)),
              flush=True)
        t_start = time()
        for exp_lib in self._exp_lib_list:
            exp_lib_zero_count = 0.0
            df = df.loc[(df.loc[:, exp_lib] > exp_lib_zero_count), :]
        t_end = time()
        print("Removal took {} seconds. DataFrame contains now {} rows.".
              format((t_end-t_start), len(df)), flush=True)
        if df.empty:
            return df
        initial_window_df = df.copy()
        # normalize counts on initial windows
        initial_window_df[self._lib_names_list] = initial_window_df[
            self._lib_names_list].div(self._size_factors, axis='columns')
        # minimum expression cutoff based on mean over experiment libraries
        print("Removing windows based on mad cutoff from DataFrame "
              "with {} rows...".format(len(df)), flush=True)
        t_start = time()
        median_abs_dev_from_zero = mad(initial_window_df.loc[
            :, self._exp_lib_list].mean(axis=1), center=0.0)
        min_expr = (self._mad_multiplier * median_abs_dev_from_zero)
        print("Minimal window expression based on mean over RIP/CLIP "
              "libraries: {} (MAD from zero: {})".format(
                  min_expr, median_abs_dev_from_zero), flush=True)
        df = df.loc[initial_window_df.loc[:, self._exp_lib_list].mean(
            axis=1) >= min_expr, :]
        t_end = time()
        print("Removal took {} seconds. DataFrame contains now {} rows.".
              format((t_end-t_start), len(df)), flush=True)
        return df 

def _generate_peak_counts(self):
        print("* Peak read counting started for {} libraries...".format(len(
            self._lib_dict)), flush=True)
        t_start = time()
        for lib_name, lib in self._lib_dict.items():
            print(lib_name, flush=True)
            for replicon in self._replicon_dict:
                lib.replicon_dict[replicon]["peak_df"] = self._replicon_dict[
                    replicon]["peak_df"]
            lib.count_reads_for_peaks()
        t_end = time()
        print("Peak read counting finished in {} seconds.".format
              (t_end-t_start), flush=True) 

def _generate_peak_counts(self):
        print("** Peak read counting started for {} libraries...".format(
            len(self._lib_dict)), flush=True)
        t_start = time()
        for lib_name, lib in self._lib_dict.items():
            print(lib_name, flush=True)
            for replicon in self._replicon_dict:
                lib.replicon_dict[replicon]["peak_df"] = self._replicon_dict[
                    replicon]["peak_df"]
            lib.count_reads_for_peaks()
        t_end = time()
        print("Peak read counting finished in {} seconds.".format(
            t_end-t_start), flush=True) 

def _filter_peaks_without_replicates(self, df):
        # calculate mad for original data frame
        median_abs_dev_from_zero = mad(df.loc[:, self._exp_lib_list].mean(
            axis=1), center=0.0)
        # minimum expression cutoff based on mean over experiment libraries
        print("Removing peaks based on mad cutoff from DataFrame "
              "with {} rows...".format(len(df)), flush=True)
        t_start = time()
        min_expr = (self._mad_multiplier * median_abs_dev_from_zero)
        print("Minimal peak expression based on mean over RIP/CLIP "
              "libraries:" "{} (MAD from zero: {})".format(
                  min_expr, median_abs_dev_from_zero), flush=True)
        df = df.loc[df.loc[:, self._exp_lib_list].mean(axis=1) >= min_expr, :]
        t_end = time()
        print("Removal took {} seconds. DataFrame contains now {} rows.".
              format((t_end-t_start), len(df)), flush=True)
        if df.empty:
            return df
        # minimum fold change
        print("Removing windows based on minimum fold change from DataFrame "
              "with {} rows...".format(len(df)), flush=True)
        t_start = time()
        df = df.query('fold_change >= @self._fc_cutoff')
        t_end = time()
        print("Removal took {} seconds. DataFrame contains now {} rows.".
              format((t_end-t_start), len(df)), flush=True)
        return df 

def generate_normalized_wiggle_files(project_folder, max_proc):
    parameter_dict = _read_parameters(project_folder)
    # create normalized coverage folder if it does not exist
    wiggle_folder = "{}/normalized_coverage".format(project_folder)
    if not exists(wiggle_folder):
        makedirs(wiggle_folder)
    # Generate coverage files in parallel
    print("** Generating normalized coverage files for {} libraries...".format(
          len(parameter_dict["libraries"])), flush=True)
    t_start = time()
    with futures.ProcessPoolExecutor(
            max_workers=max_proc) as executor:
        future_to_lib_name = {
            executor.submit(
                _generate_normalized_wiggle_file_for_lib, lib_name,
                lib["bam_file"], parameter_dict["paired_end"],
                parameter_dict["max_insert_size"], lib["size_factor"],
                wiggle_folder): lib_name for lib_name, lib
            in parameter_dict["libraries"].items()}
    for future in futures.as_completed(future_to_lib_name):
        lib_name = future_to_lib_name[future]
        print("* Coverage files for library {} generated.".format(lib_name),
              flush=True)
    t_end = time()
    print("Coverage file generation finished in {} seconds.".format(
        t_end-t_start), flush=True) 

def _init_replicons(self):
        print("** Initializing replicons and reading annotations from .gff "
              "files if present...", flush=True)
        t_start = time()
        replicons = Replicons(self._args.ctr_libs, self._args.exp_libs,
                              self._args.gff_folder, self._args.features,
                              self._args.sub_features)
        replicons.init_replicons()
        t_end = time()
        print("Finished replicon initialization in {} seconds.\n".format(
            t_end-t_start), flush=True)
        return replicons 

def _calc_sig_peaks_adaptive(self, adaptive, size_factors):
        # Run DESeq2 only if >1 libraries are available for both, experiment
        # and control libraries
        if len(self._args.exp_libs) > 1 and len(self._args.ctr_libs) > 1:
            print("** Calculating peak significance with DESeq2...",
                  flush=True)
            t_start = time()
            adaptive.run_deseq2_analysis(size_factors,
                                         self._args.pairwise_replicates)
            t_end = time()
            print("DESeq2 finished in {} seconds.\n".format(t_end-t_start),
                  flush=True)
        # If at least one control is available use fold change and MAD to
        # define significant peaks
        elif self._args.ctr_libs:
            print("** Calculating peak significance for insufficient "
                  "replicates based on fold change and MAD cutoff...",
                  flush=True)
            t_start = time()
            adaptive.run_analysis_without_replicates(size_factors)
            t_end = time()
            print("Peak calculation finished in {} seconds.\n".format(
                t_end-t_start), flush=True)
        # If no controls are available return initial peaks based on MAD cutoff
        else:
            print("** Calculating peaks without control based on MAD "
                  "cutoff...", flush=True)
            t_start = time()
            adaptive.run_analysis_without_control(size_factors)
            t_end = time()
            print("Peak calculation finished in {} seconds.\n".format(
                t_end-t_start), flush=True) 

def log(info):
    """simple log"""
    print time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()), info
    sys.stdout.flush() 

def study(self, course_id):
        """study one course"""
        time_step = 180
        log('start course:%s' % course_id)
        self.http.post(self.apis['enter_course'] % course_id, json_ret=False)
        course_show_api = self.apis['course_show'] % course_id
        log('url:%s' % course_show_api)
        self.http.post(course_show_api, json_ret=False)
        items_list = self.get_course_items(course_id)
        log('*' * 50)
        log('共有 %s 个子课程' % len(items_list))
        for index, i in enumerate(items_list):
            log('%s、%s ' % (index + 1, i['name']))
        log('*' * 50)
        log('begin to start...')
        for index, i in enumerate(items_list):
            sco_id = i['scoId']
            log('begin to study:%s-%s %s' % (index + 1, i['name'], sco_id))
            location = self.select_score_item(course_id, sco_id)
            cnt = 0
            while True:
                location = location + time_step * cnt
                cnt += 1
                log('location: %s' % location)
                self.do_heartbeat()
                self.update_timestep()
                ret = self.do_save(course_id, sco_id, location)
                if ret:
                    log('%s-%s %s' % (course_id, sco_id, 'done, start next'))
                    break
                log('*********** study %ss then go on *************' % time_step)
                time.sleep(time_step)
        info = '\033[92m\tDONE COURSE, url:%s\033[0m' % course_show_api
        log(info) 

def run(self):
        """入口"""
        s = time.time()
        course_list = []
        with open('study.list') as f:
            for course in f:
                course_list.append(course.strip())
        self.do_login()
        for course_id in course_list:
            try:
                self.study(course_id)
            except Exception as e:
                log("exception occured, study next..")
        cost = int(time.time() - s)
        log('main end, cost: %ss' % cost) 

def _ingest_summary(self):
        # get date parameters.
        yr = self._date[:4]
        mn = self._date[4:6]
        dy = self._date[6:]

        self._logger.info("Getting ingest summary data for the day")
        
        ingest_summary_cols = ["date","total"]		
        result_rows = []        
        df_filtered =  pd.DataFrame()

        query_to_load = ("""
            SELECT frame_time, COUNT(*) as total FROM {0}.{1}
            WHERE y={2} AND m={3} AND d={4} AND unix_tstamp IS NOT NULL
            AND frame_time IS NOT NULL AND frame_len IS NOT NULL
            AND dns_qry_name IS NOT NULL AND ip_src IS NOT NULL
            AND (dns_qry_class IS NOT NULL AND dns_qry_type IS NOT NULL
            AND dns_qry_rcode IS NOT NULL ) GROUP BY frame_time;
        """).format(self._db,self._table_name, yr, mn, dy)

        results = impala.execute_query_as_list(query_to_load)
        df = pd.DataFrame(results)

        # Forms a new dataframe splitting the minutes from the time column
        df_new = pd.DataFrame([["{0}-{1}-{2} {3}:{4}".format(yr, mn, dy,\
            val['frame_time'].replace("  "," ").split(" ")[3].split(":")[0].zfill(2),\
            val['frame_time'].replace("  "," ").split(" ")[3].split(":")[1].zfill(2)),\
            int(val['total']) if not math.isnan(val['total']) else 0 ] for key,val in df.iterrows()],columns = ingest_summary_cols)

        #Groups the data by minute
        sf = df_new.groupby(by=['date'])['total'].sum()
        df_per_min = pd.DataFrame({'date':sf.index, 'total':sf.values})

        df_final = df_filtered.append(df_per_min, ignore_index=True).to_records(False,False)

        if len(df_final) > 0:
            query_to_insert=("""
                INSERT INTO {0}.dns_ingest_summary PARTITION (y={1}, m={2}, d={3}) VALUES {4};
            """).format(self._db, yr, mn, dy, tuple(df_final))
            impala.execute_query(query_to_insert) 

def to_unixtime(time_string, format_string):
    with _STRPTIME_LOCK:
        return int(calendar.timegm(time.strptime(str(time_string), format_string))) 

def make_signature(access_key_secret, date=None):
    if isinstance(date, bytes):
        date = bytes.decode(date)
    if isinstance(date, int):
        date_gmt = http_date(date)
    elif date is None:
        date_gmt = http_date(int(time.time()))
    else:
        date_gmt = date

    data = str(access_key_secret) + "\n" + date_gmt
    return content_md5(data) 

def _gen_parameters(self, api, text, cusid):
        timestamp_ms = (int(time.time() * 1000))
        params = {'appkey': self.app_key,
                  'api': api,
                  'timestamp': timestamp_ms,
                  'sign': self._gen_sign(api, timestamp_ms),
                  'rq': self._gen_rq(text)}
        if cusid is not None:
            params.update(cusid=cusid)
        return params 

def add_player(self, name):
        with open(self.log_file, 'r+') as log:
            with LockFile(log):
                self._load_from_json(log)
                event = AddPlayerEvent(name, time.time())
                ret = event.process(self.players, self.games)
                self.events.append(event)
                log.seek(0)
                self._save_to_log(log)

        return ret 

def add_game(self, command_words):
        with open(self.log_file, 'r+') as log:
            with LockFile(log):
                self._load_from_json(log)
                event = AddGameEvent(command_words, time.time())
                ret = event.process(self.players, self.games)
                self.events.append(event)
                log.seek(0)
                self._save_to_log(log)

        return ret 

def to_json(self):
        ret = {}
        ret['type'] = 'AddPlayerEvent'
        ret['player'] = self.name
        ret['time'] = self.create_time
        return ret 

def from_json(the_json):
        assert the_json['type'] == 'AddPlayerEvent'
        return AddPlayerEvent(the_json['player'], the_json['time']) 

def to_json(self):
        ret = {}
        ret['type'] = 'AddGameEvent'
        ret['command'] = " ".join(self.command_words)
        ret['time'] = self.create_time
        return ret 

def __init__(self):
        self.command = ["close_game",
                        "disrupt",
                        "class_warfare",
                        "sigma",
                        "time",
                        "variety",
                        "default",
                        "slow",
                        ] 

def __init__(self, io, max=0):  # type: (IO, int) -> None
        """
        Constructor.
        """

        self._io = io
        self._max = 0
        self._step_width = None
        self._set_max_steps(max)
        self._step = 0
        self._percent = 0.0
        self._format = None
        self._internal_format = None
        self._format_line_count = 0
        self._last_messages_length = 0
        self._should_overwrite = True

        if not self._io.error_output.supports_ansi():
            # Disable overwrite when output does not support ANSI codes.
            self._should_overwrite = False

            # Set a reasonable redraw frequency so output isn't flooded
            self.set_redraw_frequency(max / 10)

        self._messages = {}

        self._start_time = time.time()