Python collections.OrderedDict() Examples

The following are 30 code examples for showing how to use collections.OrderedDict(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: BERT-Classification-Tutorial   Author: Socialbird-AILab   File: modeling.py    License: Apache License 2.0 6 votes vote down vote up
def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
    """Compute the union of the current variables and checkpoint variables."""
    assignment_map = {}
    initialized_variable_names = {}

    name_to_variable = collections.OrderedDict()
    for var in tvars:
        name = var.name
        m = re.match("^(.*):\\d+$", name)
        if m is not None:
            name = m.group(1)
        name_to_variable[name] = var

    init_vars = tf.train.list_variables(init_checkpoint)

    assignment_map = collections.OrderedDict()
    for x in init_vars:
        (name, var) = (x[0], x[1])
        if name not in name_to_variable:
            continue
        assignment_map[name] = name
        initialized_variable_names[name] = 1
        initialized_variable_names[name + ":0"] = 1

    return (assignment_map, initialized_variable_names) 
Example 2
Project: svviz   Author: svviz   File: datahub.py    License: MIT License 6 votes vote down vote up
def __init__(self):
        self.args = None
        self.alignDistance = 0
        self.samples = collections.OrderedDict()
        self.genome = None
        self.sources = {}
        self.annotationSets = collections.OrderedDict()

        # for storing axes, annotations, etc, by allele
        self.alleleTracks = collections.defaultdict(collections.OrderedDict)
        self.trackCompositor = None

        self.dotplots = {}
        self.info = {}

        self.reset() 
Example 3
Project: svviz   Author: svviz   File: track.py    License: MIT License 6 votes vote down vote up
def __init__(self, chromPartsCollection, pixelWidth, dividerSize=25):
        # length is in genomic coordinates, starts is in pixels
        self.dividerSize = dividerSize
        self.partsToLengths = collections.OrderedDict()
        self.partsToStartPixels = collections.OrderedDict()
        self.chromPartsCollection = chromPartsCollection

        for part in chromPartsCollection:
            self.partsToLengths[part.id] = len(part)

        self.pixelWidth = pixelWidth

        totalLength = sum(self.partsToLengths.values()) + (len(self.partsToLengths)-1)*dividerSize
        self.basesPerPixel = totalLength / float(pixelWidth)

        curStart = 0
        for regionID in self.partsToLengths:
            self.partsToStartPixels[regionID] = curStart
            curStart += (self.partsToLengths[regionID]+dividerSize) / self.basesPerPixel 
Example 4
Project: natural-questions   Author: google-research-datasets   File: nq_eval.py    License: Apache License 2.0 6 votes vote down vote up
def compute_f1(answer_stats, prefix=''):
  """Computes F1, precision, recall for a list of answer scores.

  Args:
    answer_stats: List of per-example scores.
    prefix (''): Prefix to prepend to score dictionary.

  Returns:
    Dictionary mapping string names to scores.
  """

  has_gold, has_pred, is_correct, _ = list(zip(*answer_stats))
  precision = safe_divide(sum(is_correct), sum(has_pred))
  recall = safe_divide(sum(is_correct), sum(has_gold))
  f1 = safe_divide(2 * precision * recall, precision + recall)

  return OrderedDict({
      prefix + 'n': len(answer_stats),
      prefix + 'f1': f1,
      prefix + 'precision': precision,
      prefix + 'recall': recall
  }) 
Example 5
Project: natural-questions   Author: google-research-datasets   File: nq_eval.py    License: Apache License 2.0 6 votes vote down vote up
def get_metrics_with_answer_stats(long_answer_stats, short_answer_stats):
  """Generate metrics dict using long and short answer stats."""

  def _get_metric_dict(answer_stats, prefix=''):
    """Compute all metrics for a set of answer statistics."""
    opt_result, pr_table = compute_pr_curves(
        answer_stats, targets=[0.5, 0.75, 0.9])
    f1, precision, recall, threshold = opt_result
    metrics = OrderedDict({
        'best-threshold-f1': f1,
        'best-threshold-precision': precision,
        'best-threshold-recall': recall,
        'best-threshold': threshold,
    })
    for target, recall, precision, _ in pr_table:
      metrics['recall-at-precision>={:.2}'.format(target)] = recall
      metrics['precision-at-precision>={:.2}'.format(target)] = precision

    # Add prefix before returning.
    return dict([(prefix + k, v) for k, v in six.iteritems(metrics)])

  metrics = _get_metric_dict(long_answer_stats, 'long-')
  metrics.update(_get_metric_dict(short_answer_stats, 'short-'))
  return metrics 
Example 6
Project: disentangling_conditional_gans   Author: zalandoresearch   File: tfutil.py    License: MIT License 6 votes vote down vote up
def _init_fields(self):
        self.name               = None          # User-specified name, defaults to build func name if None.
        self.scope              = None          # Unique TF graph scope, derived from the user-specified name.
        self.static_kwargs      = dict()        # Arguments passed to the user-supplied build func.
        self.num_inputs         = 0             # Number of input tensors.
        self.num_outputs        = 0             # Number of output tensors.
        self.input_shapes       = [[]]          # Input tensor shapes (NC or NCHW), including minibatch dimension.
        self.output_shapes      = [[]]          # Output tensor shapes (NC or NCHW), including minibatch dimension.
        self.input_shape        = []            # Short-hand for input_shapes[0].
        self.output_shape       = []            # Short-hand for output_shapes[0].
        self.input_templates    = []            # Input placeholders in the template graph.
        self.output_templates   = []            # Output tensors in the template graph.
        self.input_names        = []            # Name string for each input.
        self.output_names       = []            # Name string for each output.
        self.vars               = OrderedDict() # All variables (localname => var).
        self.trainables         = OrderedDict() # Trainable variables (localname => var).
        self._build_func        = None          # User-supplied build function that constructs the network.
        self._build_func_name   = None          # Name of the build function.
        self._build_module_src  = None          # Full source code of the module containing the build function.
        self._run_cache         = dict()        # Cached graph data for Network.run(). 
Example 7
Project: mmdetection   Author: open-mmlab   File: dist_utils.py    License: Apache License 2.0 6 votes vote down vote up
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
    if bucket_size_mb > 0:
        bucket_size_bytes = bucket_size_mb * 1024 * 1024
        buckets = _take_tensors(tensors, bucket_size_bytes)
    else:
        buckets = OrderedDict()
        for tensor in tensors:
            tp = tensor.type()
            if tp not in buckets:
                buckets[tp] = []
            buckets[tp].append(tensor)
        buckets = buckets.values()

    for bucket in buckets:
        flat_tensors = _flatten_dense_tensors(bucket)
        dist.all_reduce(flat_tensors)
        flat_tensors.div_(world_size)
        for tensor, synced in zip(
                bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
            tensor.copy_(synced) 
Example 8
Project: mmdetection   Author: open-mmlab   File: test_wrappers.py    License: Apache License 2.0 6 votes vote down vote up
def test_max_pool_2d():
    test_cases = OrderedDict([('in_w', [10, 20]), ('in_h', [10, 20]),
                              ('in_channel', [1, 3]), ('out_channel', [1, 3]),
                              ('kernel_size', [3, 5]), ('stride', [1, 2]),
                              ('padding', [0, 1]), ('dilation', [1, 2])])

    for in_h, in_w, in_cha, out_cha, k, s, p, d in product(
            *list(test_cases.values())):
        # wrapper op with 0-dim input
        x_empty = torch.randn(0, in_cha, in_h, in_w, requires_grad=True)
        wrapper = MaxPool2d(k, stride=s, padding=p, dilation=d)
        wrapper_out = wrapper(x_empty)

        # torch op with 3-dim input as shape reference
        x_normal = torch.randn(3, in_cha, in_h, in_w)
        ref = nn.MaxPool2d(k, stride=s, padding=p, dilation=d)
        ref_out = ref(x_normal)

        assert wrapper_out.shape[0] == 0
        assert wrapper_out.shape[1:] == ref_out.shape[1:]

        assert torch.equal(wrapper(x_normal), ref_out) 
Example 9
Project: mmdetection   Author: open-mmlab   File: regnet2mmdet.py    License: Apache License 2.0 6 votes vote down vote up
def convert(src, dst):
    """Convert keys in pycls pretrained RegNet models to mmdet style."""
    # load caffe model
    regnet_model = torch.load(src)
    blobs = regnet_model['model_state']
    # convert to pytorch style
    state_dict = OrderedDict()
    converted_names = set()
    for key, weight in blobs.items():
        if 'stem' in key:
            convert_stem(key, weight, state_dict, converted_names)
        elif 'head' in key:
            convert_head(key, weight, state_dict, converted_names)
        elif key.startswith('s'):
            convert_reslayer(key, weight, state_dict, converted_names)

    # check if all layers are converted
    for key in blobs:
        if key not in converted_names:
            print(f'not converted: {key}')
    # save checkpoint
    checkpoint = dict()
    checkpoint['state_dict'] = state_dict
    torch.save(checkpoint, dst) 
Example 10
Project: ciocheck   Author: ContinuumIO   File: main.py    License: MIT License 6 votes vote down vote up
def __init__(self, cmd_root, cli_args, folders=None, files=None):
        """Main tool runner."""
        # Run options
        self.cmd_root = cmd_root  # Folder on which the command was executed
        self.config = load_config(cmd_root, cli_args)
        self.file_manager = FileManager(folders=folders, files=files)
        self.folders = folders
        self.files = files
        self.all_results = OrderedDict()
        self.all_tools = {}
        self.test_results = None
        self.failed_checks = set()

        self.check = self.config.get_value('check')
        self.enforce = self.config.get_value('enforce')
        self.diff_mode = self.config.get_value('diff_mode')
        self.file_mode = self.config.get_value('file_mode')
        self.branch = self.config.get_value('branch')
        self.disable_formatters = cli_args.disable_formatters
        self.disable_linters = cli_args.disable_linters
        self.disable_tests = cli_args.disable_tests 
Example 11
Project: ciocheck   Author: ContinuumIO   File: tools.py    License: MIT License 6 votes vote down vote up
def parse_coverage(self):
        """Parse .coverage json report generated by coverage."""
        coverage_string = ("!coverage.py: This is a private format, don't "
                           "read it directly!")
        coverage_path = os.path.join(self.cmd_root, '.coverage')

        covered_lines = {}
        if os.path.isfile(coverage_path):
            with open(coverage_path, 'r') as file_obj:
                data = file_obj.read()
                data = data.replace(coverage_string, '')

            cov = json.loads(data)
            covered_lines = OrderedDict()
            lines = cov['lines']
            for path in sorted(lines):
                covered_lines[path] = lines[path]
        return covered_lines 
Example 12
Project: neural-fingerprinting   Author: StephanZheng   File: madry_mnist_model.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def fprop(self, x):

        output = OrderedDict()
        # first convolutional layer
        h_conv1 = tf.nn.relu(self._conv2d(x, self.W_conv1) + self.b_conv1)
        h_pool1 = self._max_pool_2x2(h_conv1)

        # second convolutional layer
        h_conv2 = tf.nn.relu(
            self._conv2d(h_pool1, self.W_conv2) + self.b_conv2)
        h_pool2 = self._max_pool_2x2(h_conv2)

        # first fully connected layer

        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, self.W_fc1) + self.b_fc1)

        # output layer
        logits = tf.matmul(h_fc1, self.W_fc2) + self.b_fc2

        output = deterministic_dict(locals())
        del output["self"]
        output[self.O_PROBS] = tf.nn.softmax(logits=logits)

        return output 
Example 13
Project: models   Author: kipoi   File: dataloader_m.py    License: MIT License 6 votes vote down vote up
def read_cpg_profiles(filenames, log=None, *args, **kwargs):
    """Read methylation profiles.

    Input files can be gzip compressed.

    Returns
    -------
    dict
        `dict (key, value)`, where `key` is the output name and `value` the CpG
        table.
    """

    cpg_profiles = OrderedDict()
    for filename in filenames:
        if log:
            log(filename)
        #cpg_file = dat.GzipFile(filename, 'r')
        cpg_file = get_fh(filename, 'r')
        output_name = split_ext(filename)
        cpg_profile = dat.read_cpg_profile(cpg_file, sort=True, *args, **kwargs)
        cpg_profiles[output_name] = cpg_profile
        cpg_file.close()
    return cpg_profiles 
Example 14
Project: models   Author: kipoi   File: dataloader_m.py    License: MIT License 6 votes vote down vote up
def map_cpg_tables(cpg_tables, chromo, chromo_pos):
    """Maps values from cpg_tables to `chromo_pos`.

    Positions in `cpg_tables` for `chromo`  must be a subset of `chromo_pos`.
    Inserts `dat.CPG_NAN` for uncovered positions.
    """
    chromo_pos.sort()
    mapped_tables = OrderedDict()
    for name, cpg_table in six.iteritems(cpg_tables):
        cpg_table = cpg_table.loc[cpg_table.chromo == chromo]
        cpg_table = cpg_table.sort_values('pos')
        mapped_table = map_values(cpg_table.value.values,
                                  cpg_table.pos.values,
                                  chromo_pos)
        assert len(mapped_table) == len(chromo_pos)
        mapped_tables[name] = mapped_table
    return mapped_tables 
Example 15
Project: deep-learning-note   Author: wdxtub   File: 10_two_layer_net.py    License: MIT License 6 votes vote down vote up
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
        # 初始化权重
        self.params = {}
        # 用高斯分布初始化
        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
        self.params['b1'] = np.zeros(hidden_size)
        self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
        self.params['b2'] = np.zeros(output_size)

        # 生成层
        self.layers = OrderedDict()
        self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
        self.layers['Relu1'] = Relu()
        self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])

        self.lastLayer = SoftmaxWithLoss() 
Example 16
Project: deep-learning-note   Author: wdxtub   File: multi_layer_net.py    License: MIT License 6 votes vote down vote up
def __init__(self, input_size, hidden_size_list, output_size,
                 activation='relu', weight_init_std='relu', weight_decay_lambda=0):
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_size_list = hidden_size_list
        self.hidden_layer_num = len(hidden_size_list)
        self.weight_decay_lambda = weight_decay_lambda
        self.params = {}

        # 初始化权重
        self.__init_weight(weight_init_std)

        # 生成层
        activation_layer = {'sigmoid': Sigmoid, 'relu': Relu}
        self.layers = OrderedDict()
        for idx in range(1, self.hidden_layer_num+1):
            self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)],
                                                      self.params['b' + str(idx)])
            self.layers['Activation_function' + str(idx)] = activation_layer[activation]()

        idx = self.hidden_layer_num + 1
        self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)],
            self.params['b' + str(idx)])

        self.last_layer = SoftmaxWithLoss() 
Example 17
Project: video2commons   Author: toolforge   File: urlextract.py    License: GNU General Public License v3.0 6 votes vote down vote up
def escape_wikitext(wikitext):
    """Escape wikitext for use in file description."""
    rep = OrderedDict([
        ('{|', '{{(}}|'),
        ('|}', '|{{)}}'),
        ('||', '||'),
        ('|', '|'),
        ('[[', '{{!((}}'),
        (']]', '{{))!}}'),
        ('{{', '{{((}}'),
        ('}}', '{{))}}'),
        ('{', '{{(}}'),
        ('}', '{{)}}'),
    ])
    rep = dict((re.escape(k), v) for k, v in rep.iteritems())
    pattern = re.compile("|".join(rep.keys()))
    return pattern.sub(lambda m: rep[re.escape(m.group(0))], wikitext)


# Source: mediawiki.Title.js@9df363d 
Example 18
Project: BERT-Classification-Tutorial   Author: Socialbird-AILab   File: tokenization.py    License: Apache License 2.0 5 votes vote down vote up
def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    index = 0
    with tf.gfile.GFile(vocab_file, "r") as reader:
        while True:
            token = convert_to_unicode(reader.readline())
            if not token:
                break
            token = token.strip()
            vocab[token] = index
            index += 1
    return vocab 
Example 19
Project: BERT-Classification-Tutorial   Author: Socialbird-AILab   File: run_classifier.py    License: Apache License 2.0 5 votes vote down vote up
def file_based_convert_examples_to_features(
        examples, label_list, max_seq_length, tokenizer, output_file):
    """Convert a set of `InputExample`s to a TFRecord file."""

    writer = tf.python_io.TFRecordWriter(output_file)
    
    label_map = {}
    for (i, label) in enumerate(sorted(label_list)):
        label_map[label] = i
        
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

        feature = convert_single_example(ex_index, example, label_map,
                                                    max_seq_length, tokenizer)

        def create_int_feature(values):
            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
            return f

        features = collections.OrderedDict()
        features["input_ids"] = create_int_feature(feature.input_ids)
        features["input_mask"] = create_int_feature(feature.input_mask)
        features["segment_ids"] = create_int_feature(feature.segment_ids)
        features["label_ids"] = create_int_feature([feature.label_id])

        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(tf_example.SerializeToString())
    return label_map 
Example 20
Project: indras_net   Author: gcallah   File: composite.py    License: GNU General Public License v3.0 5 votes vote down vote up
def __init__(self, name, attrs=None, members=None,
                 duration=INF, action=None, member_creator=None,
                 num_members=None, serial_obj=None,
                 reg=True, **kwargs):

        self.num_members_ever = 0
        self.members = OrderedDict()
        super().__init__(name, attrs=attrs, duration=duration,
                         action=action, serial_obj=serial_obj,
                         reg=False)

        self.type = type(self).__name__

        if serial_obj is not None:
            self.restore(serial_obj)
        else:
            if members is not None:
                for member in members:
                    join(self, member)
            if num_members is None:
                num_members = 1  # A default if they forgot to pass this.
            self.num_members_ever = num_members
            self.member_creator = None
            if member_creator is not None:
                self.member_creator = member_creator
                # If we have a member creator function, call it
                # `num_members` times to create group members.
                for i in range(num_members):
                    join(self, member_creator(self.name, i, **kwargs))
        if reg:
            add_group(self.name, self) 
Example 21
Project: indras_net   Author: gcallah   File: composite.py    License: GNU General Public License v3.0 5 votes vote down vote up
def subset(self, predicate, *args, name=None):  # noqa E999
        new_dict = OrderedDict()
        for mbr in self:
            if predicate(self[mbr], *args):
                new_dict[mbr] = self[mbr]
        return grp_from_nm_dict(name, new_dict) 
Example 22
Project: indras_net   Author: gcallah   File: menu.py    License: GNU General Public License v3.0 5 votes vote down vote up
def __init__(self, name, user, level=0):
        super().__init__(name)
        self.user = user
        self.menu_items = OrderedDict()
        self.def_act = None
        self.level = level 
Example 23
Project: indras_net   Author: gcallah   File: agent_pop.py    License: GNU General Public License v3.0 5 votes vote down vote up
def __init__(self, name):
        super().__init__(name)
        self.vars = OrderedDict()
        self.graph = nx.Graph() 
Example 24
Project: indras_net   Author: gcallah   File: test_agent_pop.py    License: GNU General Public License v3.0 5 votes vote down vote up
def __init__(self, methodName):
        super().__init__(methodName=methodName)
        # A dictionary for different types of dummy agents in order to test agent pop class
        # First we will fill this dict with various dummy agents,
        # then we will fill the Agent Pop class, using dict_for_reference as source
        # The OrderedDict in Agent Pop should be the same as dic_for_reference. That will test if append works
        # If append works and we have the OrderedDict in Agent Pop,
        # we will test rest of the functions if they return correct result, with reference of dict_for_reference
        # format: {var:{"agents": [], "pop_data": 0,"pop_hist": [],"my_periods": 0,"disp_color": None}}
        self.dic_for_reference = OrderedDict()
        self.agentpop = ap.AgentPop("test")
        # add 3 varieties
        for i in range(3):
            self.dic_for_reference[str(i)] = {AGENTS: [], POP_DATA: 0,POP_HIST: [],MY_PERIODS: 0,DISP_COLOR: None}
        for i in range(3):
            # add color to each variety
            self.dic_for_reference[str(i)][DISP_COLOR] = "#" + str(i)
            # add agents; agent type is Node
            for r in range(random.randint(1,7)):
                self.dic_for_reference[str(i)][AGENTS].append(node.Node("agent"+str(r)))
        for var in self.dic_for_reference:
            for a in self.dic_for_reference[var][AGENTS]:
                a.ntype = var
                #assign ntype for each agent; each dic_for_reference's variety should be the same as its agents' ntype!
    #     finish building dic_for_reference,
    #     dic_for_reference should look like: {
    #       '0':{"agents": [1-7 agents of ntype('0')], "pop_data": 0,"pop_hist": [],"my_periods": 0,"disp_color": '#0'}
    #       '1':{"agents": [1-7 agents of ntype('1')], "pop_data": 0,"pop_hist": [],"my_periods": 0,"disp_color": '#1'}
    #       '2':{"agents": [1-7 agents of ntype('2')], "pop_data": 0,"pop_hist": [],"my_periods": 0,"disp_color": '#2'}
    # }
    # Note: each individual test function has its own unique self.dic_for_reference and self.agent_pop. Change of self.dic_for_reference
    # in one function won't affect the use of it in other functions. (Two functions will have different self.dic_for_reference) 
Example 25
Project: svviz   Author: svviz   File: variants.py    License: MIT License 5 votes vote down vote up
def __init__(self, parts=None):
        self.parts = collections.OrderedDict()
        if parts is not None:
            for part in parts:
                self.parts[part.id] = part 
Example 26
Project: svviz   Author: svviz   File: datahub.py    License: MIT License 5 votes vote down vote up
def getCounts(self):
        if self._counts is None:
            self._counts = collections.OrderedDict()
            for name, sample in self.samples.items():
                self._counts[name] = collections.Counter([alnCollection.choice for alnCollection in sample.alnCollections])
            self._counts["Total"] = dict((allele, sum(self._counts[name][allele] for name in self.samples)) 
                                          for allele in ["alt", "ref", "amb"])

        return self._counts 
Example 27
Project: svviz   Author: svviz   File: datahub.py    License: MIT License 5 votes vote down vote up
def reset(self):
        self.reads = []
        self.alnCollections = []
        self.tracks = collections.OrderedDict() 
Example 28
Project: svviz   Author: svviz   File: export.py    License: MIT License 5 votes vote down vote up
def addTrackSVG(self, section, name, tracksvg, viewbox=None, height=100):
        if not section in self.sections:
            self.sections[section] = collections.OrderedDict()
        self.sections[section][name] = {"svg":tracksvg,
                                        "viewbox":viewbox,
                                        "height": height
                                       } 
Example 29
Project: incubator-spot   Author: apache   File: flow_oa.py    License: Apache License 2.0 5 votes vote down vote up
def _initialize_members(self,date,limit,logger): 

        # get logger if exists. if not, create new instance.
        self._logger = logging.getLogger('OA.Flow') if logger else Util.get_logger('OA.Flow',create_file=False)

        # initialize required parameters.
        self._scrtip_path = os.path.dirname(os.path.abspath(__file__))
        self._date = date
        self._table_name = "flow"
        self._flow_results = []
        self._limit = limit
        self._data_path = None
        self._ipynb_path = None
        self._ingest_summary_path = None
        self._flow_scores = []
        self._results_delimiter = '\t'
        

        # get app configuration.
        self._spot_conf = Util.get_spot_conf()

        # # get scores fields conf
        conf_file = "{0}/flow_conf.json".format(self._scrtip_path)
        self._conf = json.loads(open (conf_file).read(),object_pairs_hook=OrderedDict)

        # initialize data engine
        self._db = self._spot_conf.get('conf', 'DBNAME').replace("'", "").replace('"', '') 
Example 30
Project: incubator-spot   Author: apache   File: proxy_oa.py    License: Apache License 2.0 5 votes vote down vote up
def _initialize_members(self,date,limit,logger):

        # get logger if exists. if not, create new instance.
        self._logger = logging.getLogger('OA.PROXY') if logger else Util.get_logger('OA.PROXY',create_file=False)

        # initialize required parameters.
        self._scrtip_path = os.path.dirname(os.path.abspath(__file__))
        self._date = date
        self._table_name = "proxy"
        self._proxy_results = []
        self._limit = limit
        self._data_path = None
        self._ipynb_path = None
        self._ingest_summary_path = None
        self._proxy_scores = []
        self._proxy_scores_headers = []
        self._proxy_extra_columns = []
        self._results_delimiter = '\t'

        # get app configuration.
        self._spot_conf = Util.get_spot_conf()

        # get scores fields conf
        conf_file = "{0}/proxy_conf.json".format(self._scrtip_path)
        self._conf = json.loads(open (conf_file).read(),object_pairs_hook=OrderedDict)

        # initialize data engine
        self._db = self._spot_conf.get('conf', 'DBNAME').replace("'", "").replace('"', '')