From ae86c96a81b7974205a664fe89afdcb519be75dd Mon Sep 17 00:00:00 2001 From: Martin Schrimpf Date: Wed, 16 Jan 2019 18:15:55 -0500 Subject: [PATCH] extract brainio_{base,collection} into separate repositories https://github.com/brain-score/brainio_base https://github.com/brain-score/brainio_collection --- .gitignore | 5 - LICENSE | 2 +- MANIFEST.in | 4 +- README.md | 13 +- brainscore/__init__.py | 8 +- brainscore/assemblies.py | 238 -- brainscore/fetch.py | 236 - brainscore/knownfile.db | Bin 3756032 -> 0 bytes brainscore/knownfile.py | 60 - brainscore/lookup.db | Bin 49074176 -> 0 bytes brainscore/lookup.py | 5 - brainscore/metrics/__init__.py | 3 +- brainscore/metrics/rdm.py | 2 +- brainscore/metrics/transformations.py | 3 +- brainscore/metrics/xarray_utils.py | 2 +- brainscore/neuroids.py | 16 - brainscore/stimuli.py | 127 - docs/lookup.png | Bin 175155 -> 0 bytes docs/lookup2.png | Bin 178186 -> 0 bytes environment.yml | 23 - scratch/2017-09-25_sqlite_scratch.ipynb | 674 --- scratch/2018-04-11_peewee_scratch.ipynb | 3782 ----------------- scratch/2018-04-11_peewee_scratch.py | 103 - scratch/2018-04-18_image_store.ipynb | 3064 ------------- scratch/ha_temporal_via_darren.py | 339 -- scratch/image_meta_dataframe.pkl | Bin 2190116 -> 0 bytes scratch/populate_lookup.py | 106 - setup.py | 30 +- tests/__init__.py | 1 - tests/test___init__.py | 119 - tests/test_assemblies.py | 47 - tests/test_benchmarks.py | 15 +- tests/test_fetch.py | 17 - tests/test_integration.py | 111 - tests/test_metrics/test___init__.py | 2 +- tests/test_metrics/test_ceiling.py | 2 +- .../test_metrics/test_neural_predictivity.py | 2 +- tests/test_metrics/test_rdm.py | 2 +- tests/test_metrics/test_transformations.py | 2 +- tests/test_metrics/test_xarray_utils.py | 2 +- tests/test_stimuli.py | 61 - 41 files changed, 31 insertions(+), 9197 deletions(-) delete mode 100644 .gitignore delete mode 100644 brainscore/fetch.py delete mode 100644 brainscore/knownfile.db delete mode 100644 brainscore/knownfile.py delete mode 100755 brainscore/lookup.db delete mode 100644 brainscore/lookup.py delete mode 100644 brainscore/neuroids.py delete mode 100644 brainscore/stimuli.py delete mode 100644 docs/lookup.png delete mode 100644 docs/lookup2.png delete mode 100644 environment.yml delete mode 100644 scratch/2017-09-25_sqlite_scratch.ipynb delete mode 100644 scratch/2018-04-11_peewee_scratch.ipynb delete mode 100644 scratch/2018-04-11_peewee_scratch.py delete mode 100644 scratch/2018-04-18_image_store.ipynb delete mode 100644 scratch/ha_temporal_via_darren.py delete mode 100644 scratch/image_meta_dataframe.pkl delete mode 100644 scratch/populate_lookup.py delete mode 100644 tests/test___init__.py delete mode 100644 tests/test_assemblies.py delete mode 100644 tests/test_fetch.py delete mode 100644 tests/test_integration.py delete mode 100644 tests/test_stimuli.py diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 08de48274..000000000 --- a/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -*.pyc - -__pycache__ -mkgu.egg-info -.idea diff --git a/LICENSE b/LICENSE index 7539d6d20..2cae7cd4b 100644 --- a/LICENSE +++ b/LICENSE @@ -1,7 +1,7 @@ MIT License -Copyright (c) 2017, Jon Prescott-Roy +Copyright (c) 2017, Martin Schrimpf and Jon Prescott-Roy Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: diff --git a/MANIFEST.in b/MANIFEST.in index ee60069cd..fa01dd888 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,10 +1,8 @@ include LICENSE -include README.rst +include README.md recursive-include tests * recursive-exclude * __pycache__ recursive-exclude * *.py[co] recursive-include docs *.rst conf.py Makefile make.bat *.jpg *.png *.gif - -include brainscore/lookup.db diff --git a/README.md b/README.md index b8ba8b973..94379a5c8 100644 --- a/README.md +++ b/README.md @@ -6,15 +6,15 @@ Brain recordings (termed "assemblies", e.g. neural or behavioral) are packaged in a [standard format](http://xarray.pydata.org/). This allows metrics (e.g. neural predictivity, RDMs) to operate on many assemblies without having to be re-written. -Together with http://github.com/dicarlolab/candidate_models, `brainscore` +Together with http://github.com/brain-score/candidate_models, `brainscore` allows scoring candidate models of the brain on a range of assemblies and metrics. ## Quick setup -Recommended for most users. Use Brain-Score as a library. You will need Python >= 3.6. +Recommended for most users. Use Brain-Score as a library. You will need Python >= 3.6 and pip >= 18.1. -`pip install --process-dependency-links git+https://github.com/dicarlolab/brain-score` +`pip install git+https://github.com/brain-score/brain-score` To contribute code to Brain-Score, see the [Development Setup](#development-setup). @@ -34,7 +34,7 @@ Coordinates: - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ... ... $ ... -$ metric = RDMCrossValidated() +$ metric = RDM() $ score = metric(assembly1=hvm, assembly2=hvm) Score(aggregation: 2)> array([1., 0.]) @@ -66,9 +66,8 @@ Only necessary if you plan to change code. 2. Clone the Git repository to wherever you keep repositories: * `cd ~/dev` * `git clone git@github.com:dicarlolab/brain-score.git` -3. Create and activate a Conda environment with relevant packages: - * `conda env create -f environment.yml` - * `conda activate brainscore` +3. Install the depencies (we suggest doing this in a [conda environment](https://conda.io/docs/user-guide/tasks/manage-environments.html)): + * `pip install -e .` ## License diff --git a/brainscore/__init__.py b/brainscore/__init__.py index 957585101..71d98c146 100644 --- a/brainscore/__init__.py +++ b/brainscore/__init__.py @@ -1,10 +1,4 @@ -# -*- coding: utf-8 -*- - -__author__ = """Jon Prescott-Roy""" -__email__ = 'jjpr@mit.edu' -__version__ = '0.1.0' - -from .fetch import get_assembly, get_stimulus_set +from brainio_collection.fetch import get_assembly, get_stimulus_set from brainscore.contrib import benchmarks as contrib_benchmarks diff --git a/brainscore/assemblies.py b/brainscore/assemblies.py index c37222f6f..83d41cdca 100644 --- a/brainscore/assemblies.py +++ b/brainscore/assemblies.py @@ -1,242 +1,4 @@ -from __future__ import absolute_import, division, print_function, unicode_literals - -import functools -import operator -from collections import OrderedDict, defaultdict - -import numpy as np -import peewee import xarray as xr -from xarray import DataArray - -from brainscore.lookup import pwdb -from brainscore.stimuli import StimulusSetModel - - -class DataPoint(object): - """A DataPoint represents one value, usually a recording from one neuron or node, - in response to one presentation of a stimulus. """ - - def __init__(self, value, neuroid, presentation): - self.value = value - self.neuroid = neuroid - self.presentation = presentation - - -class DataAssembly(DataArray): - """A DataAssembly represents a set of data a researcher wishes to work with for - an analysis or benchmarking task. """ - - def __init__(self, *args, **kwargs): - super(DataAssembly, self).__init__(*args, **kwargs) - gather_indexes(self) - - def multi_groupby(self, group_coord_names, *args, **kwargs): - delimiter = "|" - multi_group_name = "multi_group" - dim = self._dim_of_group_coords(group_coord_names) - tmp_assy = self._join_group_coords(dim, group_coord_names, delimiter, multi_group_name) - result = tmp_assy.groupby(multi_group_name, *args, **kwargs) - return GroupbyBridge(result, self, dim, group_coord_names, delimiter, multi_group_name) - - def _join_group_coords(self, dim, group_coord_names, delimiter, multi_group_name): - tmp_assy = self.copy() - group_coords = [tmp_assy.coords[c] for c in group_coord_names] - multi_group_coord = [] - for coords in zip(*group_coords): - multi_group_coord.append(delimiter.join([str(c.values) for c in coords])) - tmp_assy.coords[multi_group_name] = dim, multi_group_coord - tmp_assy.set_index(append=True, inplace=True, **{dim: multi_group_name}) - return tmp_assy - - def _dim_of_group_coords(self, group_coord_names): - dimses = [self.coords[coord_name].dims for coord_name in group_coord_names] - dims = [dim for dim_tuple in dimses for dim in dim_tuple] - if len(set(dims)) == 1: - return dims[0] - else: - raise GroupbyError("All coordinates for grouping must be associated with the same single dimension. ") - - def multisel(self, method=None, tolerance=None, drop=False, **indexers): - """ - partial workaround to keep multi-indexes and scalar coords - https://github.com/pydata/xarray/issues/1491, https://github.com/pydata/xarray/pull/1426 - - this method might slow things down, use with caution - """ - indexer_dims = {index: self[index].dims for index in indexers} - dims = [] - for _dims in indexer_dims.values(): - assert len(_dims) == 1 - dims.append(_dims[0]) - coords_dim, dim_coords = {}, defaultdict(list) - for dim in dims: - for coord, coord_dims, _ in walk_coords(self): - if array_is_element(coord_dims, dim): - coords_dim[coord] = dim - dim_coords[dim].append(coord) - - result = super().sel(method=method, tolerance=tolerance, drop=drop, **indexers) - - # un-drop potentially dropped dims - for coord, value in indexers.items(): - dim = self[coord].dims - assert len(dim) == 1 - dim = dim[0] - if not hasattr(result, coord) and dim not in result.dims: - result = result.expand_dims(coord) - result[coord] = [value] - - # stack back together - stack_dims = list(result.dims) - for result_dim in stack_dims: - if result_dim not in self.dims: - original_dim = coords_dim[result_dim] - stack_coords = [coord for coord in dim_coords[original_dim] if hasattr(result, coord)] - for coord in stack_coords: - stack_dims.remove(coord) - result = result.stack(**{original_dim: stack_coords}) - # add scalar indexer variable - for index, value in indexers.items(): - if hasattr(result, index): - continue # already set, potentially during un-dropping - dim = indexer_dims[index] - assert len(dim) == 1 - value = np.repeat(value, len(result[dim[0]])) - result[index] = dim, value - return result - - -class BehavioralAssembly(DataAssembly): - """A BehavioralAssembly is a DataAssembly containing behavioral data. """ - pass - - -class NeuroidAssembly(DataAssembly): - """A NeuroidAssembly is a DataAssembly containing data recorded from either neurons - or neuron analogues. """ - pass - - -class NeuronRecordingAssembly(NeuroidAssembly): - """A NeuronRecordingAssembly is a NeuroidAssembly containing data recorded from neurons. """ - pass - - -class ModelFeaturesAssembly(NeuroidAssembly): - """A ModelFeaturesAssembly is a NeuroidAssembly containing data captured from nodes in - a machine learning model. """ - pass - - -def coords_for_dim(xr_data, dim, exclude_indexes=True): - result = OrderedDict() - for key, value in xr_data.coords.variables.items(): - only_this_dim = value.dims == (dim,) - exclude_because_index = exclude_indexes and isinstance(value, xr.IndexVariable) - if only_this_dim and not exclude_because_index: - result[key] = value - return result - - -def gather_indexes(xr_data): - """This is only necessary as long as xarray cannot persist MultiIndex to netCDF. """ - coords_d = {} - for dim in xr_data.dims: - coords = coords_for_dim(xr_data, dim) - if coords: - coords_d[dim] = list(coords.keys()) - if coords_d: - xr_data.set_index(append=True, inplace=True, **coords_d) - return xr_data - - -class GroupbyBridge(object): - """Wraps an xarray GroupBy object to allow grouping on multiple coordinates. """ - - def __init__(self, groupby, assembly, dim, group_coord_names, delimiter, multi_group_name): - self.groupby = groupby - self.assembly = assembly - self.dim = dim - self.group_coord_names = group_coord_names - self.delimiter = delimiter - self.multi_group_name = multi_group_name - - def __getattr__(self, attr): - result = getattr(self.groupby, attr) - if callable(result): - result = self.wrap_groupby(result) - return result - - def wrap_groupby(self, func): - def wrapper(*args, **kwargs): - result = func(*args, **kwargs) - if isinstance(result, type(self.assembly)): - result = self.split_group_coords(result) - return result - - return wrapper - - def split_group_coords(self, result): - split_coords = np.array( - list(map(lambda s: s.split(self.delimiter) if isinstance(s, str) else [s], - result.coords[self.multi_group_name].values))).T - for coord_name, coord in zip(self.group_coord_names, split_coords): - result.coords[coord_name] = (self.multi_group_name, coord) - result.reset_index(self.multi_group_name, drop=True, inplace=True) - result.set_index(append=True, inplace=True, **{self.multi_group_name: self.group_coord_names}) - result = result.rename({self.multi_group_name: self.dim}) - return result - - -class GroupbyError(Exception): - pass - - -class AssemblyModel(peewee.Model): - """An AssemblyModel stores information about the canonical location where the data - for a DataAssembly is stored. """ - name = peewee.CharField() - assembly_class = peewee.CharField() - stimulus_set = peewee.ForeignKeyField(StimulusSetModel, backref="assembly_models") - - class Meta: - database = pwdb - - -class AssemblyStoreModel(peewee.Model): - """An AssemblyStoreModel stores the location of a DataAssembly data file. """ - assembly_type = peewee.CharField() - location_type = peewee.CharField() - location = peewee.CharField() - unique_name = peewee.CharField(unique=True, null=True, index=True) - sha1 = peewee.CharField(unique=True, null=True, index=True) - - class Meta: - database = pwdb - - -class AssemblyStoreMap(peewee.Model): - """An AssemblyStoreMap links an AssemblyRecord to an AssemblyStore. """ - assembly_model = peewee.ForeignKeyField(AssemblyModel, backref="assembly_store_maps") - assembly_store_model = peewee.ForeignKeyField(AssemblyStoreModel, backref="assembly_store_maps") - role = peewee.CharField() - - class Meta: - database = pwdb - - -class AssemblyLookupError(Exception): - pass - - -def lookup_assembly(name): - pwdb.connect(reuse_if_open=True) - try: - assy = AssemblyModel.get(AssemblyModel.name == name) - except AssemblyModel.DoesNotExist as e: - raise AssemblyLookupError("A DataAssembly named " + name + " was not found.") - return assy def merge_data_arrays(data_arrays): diff --git a/brainscore/fetch.py b/brainscore/fetch.py deleted file mode 100644 index bd05046f1..000000000 --- a/brainscore/fetch.py +++ /dev/null @@ -1,236 +0,0 @@ -from __future__ import absolute_import, division, print_function, unicode_literals - -import hashlib -import logging -import os -import zipfile - -import boto3 -import pandas as pd -import xarray as xr -from botocore import UNSIGNED -from botocore.config import Config -from six.moves.urllib.parse import urlparse -from tqdm import tqdm - -from brainscore import assemblies -from brainscore.assemblies import coords_for_dim -from brainscore.stimuli import StimulusSet, ImageModel, AttributeModel, ImageMetaModel, StimulusSetModel, \ - ImageStoreModel, StimulusSetImageMap, ImageStoreMap -from brainscore.utils import fullname - -_local_data_path = os.path.expanduser("~/.brainscore/data") - - -class Fetcher(object): - """A Fetcher obtains data with which to populate a DataAssembly. """ - - def __init__(self, location, unique_name): - self.location = location - self.unique_name = unique_name - self.local_dir_path = os.path.join(_local_data_path, self.unique_name) - os.makedirs(self.local_dir_path, exist_ok=True) - - def fetch(self): - """ - Fetches the resource identified by location. - :return: a full local file path - """ - raise NotImplementedError("The base Fetcher class does not implement .fetch(). Use a subclass of Fetcher. ") - - -class BotoFetcher(Fetcher): - """A Fetcher that retrieves files from Amazon Web Services' S3 data storage. """ - - def __init__(self, location, unique_name): - super(BotoFetcher, self).__init__(location, unique_name) - self.parsed_url = urlparse(self.location) - self.split_hostname = self.parsed_url.hostname.split(".") - self.split_path = self.parsed_url.path.lstrip('/').split("/") - # http://docs.aws.amazon.com/AmazonS3/latest/dev/UsingBucket.html#access-bucket-intro - virtual_hosted_style = len(self.split_hostname) == 4 - if virtual_hosted_style: - self.bucketname = self.split_hostname[0] - self.relative_path = os.path.join(*(self.split_path)) - else: - self.bucketname = self.split_path[0] - self.relative_path = os.path.join(*(self.split_path[1:])) - self.basename = self.split_path[-1] - self.output_filename = os.path.join(self.local_dir_path, self.relative_path) - self._logger = logging.getLogger(fullname(self)) - - def fetch(self): - if not os.path.exists(self.output_filename): - self.download_boto() - return self.output_filename - - def download_boto(self, sha1=None): - """Downloads file from S3 via boto at `url` and writes it in `self.output_filename`.""" - self._logger.info('downloading %s' % self.relative_path) - try: # try with authentication - self._logger.debug("attempting default download (signed)") - self.download_boto_config(config=None, sha1=sha1) - except Exception: # try without authentication - self._logger.debug("default download failed, trying unsigned") - # disable signing requests. see https://stackoverflow.com/a/34866092/2225200 - unsigned_config = Config(signature_version=UNSIGNED) - self.download_boto_config(config=unsigned_config, sha1=sha1) - - def download_boto_config(self, config, sha1=None): - s3 = boto3.resource('s3', config=config) - obj = s3.Object(self.bucketname, self.relative_path) - # show progress. see https://gist.github.com/wy193777/e7607d12fad13459e8992d4f69b53586 - with tqdm(total=obj.content_length, unit='B', unit_scale=True, desc=self.relative_path) as progress_bar: - def progress_hook(bytes_amount): - progress_bar.update(bytes_amount) - - obj.download_file(self.output_filename, Callback=progress_hook) - - if sha1 is not None: - self.verify_sha1(self.output_filename, sha1) - - def verify_sha1(self, filename, sha1): - data = open(filename, 'rb').read() - if sha1 != hashlib.sha1(data).hexdigest(): - raise IOError("File '%s': invalid SHA-1 hash! You may want to delete " - "this corrupted file..." % filename) - - -class URLFetcher(Fetcher): - """A Fetcher that retrieves a resource identified by URL. """ - - def __init__(self, location, unique_name): - super(URLFetcher, self).__init__(location, unique_name) - - -class LocalFetcher(Fetcher): - """A Fetcher that retrieves local files. """ - - def __init__(self, location, unique_name): - super(LocalFetcher, self).__init__(location, unique_name) - - -class AssemblyLoader(object): - """ - Loads a DataAssembly from files. - """ - - def __init__(self, assy_model, local_paths): - self.assy_model = assy_model - self.local_paths = local_paths - - def load(self): - data_arrays = [] - for role, path in self.local_paths.items(): - tmp_da = xr.open_dataarray(path) - data_arrays.append(tmp_da) - concatenated = xr.concat(data_arrays, dim="presentation") - stimulus_set_name = self.assy_model.stimulus_set.name - stimulus_set = get_stimulus_set(stimulus_set_name) - merged = self.merge(concatenated, stimulus_set) - class_object = getattr(assemblies, self.assy_model.assembly_class) - result = class_object(data=merged) - result.attrs["stimulus_set_name"] = stimulus_set_name - result.attrs["stimulus_set"] = stimulus_set - return result - - def merge(self, assy, stimulus_set): - axis_name = "presentation" - df_of_coords = pd.DataFrame(coords_for_dim(assy, axis_name)) - merged = df_of_coords.merge(stimulus_set, on="image_id", how="left") - for col in stimulus_set.columns: - assy[col] = (axis_name, merged[col]) - # assy.set_index(append=True, inplace=True, **{axis_name: [col]}) - return assy - - -class AssemblyFetchError(Exception): - pass - - -_fetcher_types = { - "local": LocalFetcher, - "S3": BotoFetcher, - "URL": URLFetcher -} - - -def get_fetcher(type="S3", location=None, unique_name=None): - return _fetcher_types[type](location, unique_name) - - -def fetch_assembly(assy_model): - local_paths = {} - for s in assy_model.assembly_store_maps: - fetcher = get_fetcher(type=s.assembly_store_model.location_type, - location=s.assembly_store_model.location, - unique_name=s.assembly_store_model.unique_name) - local_paths[s.role] = fetcher.fetch() - return local_paths - - -def fetch_stimulus_set(stimulus_set_model): - local_paths = {} - image_paths = {} - pw_query_stores_for_set = ImageStoreModel.select() \ - .join(ImageStoreMap) \ - .join(ImageModel) \ - .join(StimulusSetImageMap) \ - .join(StimulusSetModel) \ - .where(StimulusSetModel.name == stimulus_set_model.name) \ - .distinct() - for s in pw_query_stores_for_set: - fetcher = get_fetcher(type=s.location_type, - location=s.location, - unique_name=s.unique_name) - fetched = fetcher.fetch() - containing_dir = os.path.dirname(fetched) - with zipfile.ZipFile(fetched, 'r') as zip_file: - if not all(map(lambda x: os.path.exists(os.path.join(containing_dir, x)), zip_file.namelist())): - zip_file.extractall(containing_dir) - local_paths[s.location] = containing_dir - for image_map in stimulus_set_model.stimulus_set_image_maps.prefetch(ImageModel, ImageStoreMap, ImageStoreModel): - store_map = image_map.image.image_image_store_maps[0] - local_path_base = local_paths[store_map.image_store.location] - image_path = os.path.join(local_path_base, store_map.path) - image_paths[image_map.image.image_id] = image_path - return image_paths - - -def get_assembly(name): - assy_model = assemblies.lookup_assembly(name) - local_paths = fetch_assembly(assy_model) - loader = AssemblyLoader(assy_model, local_paths) - assembly = loader.load() - assembly.name = name - return assembly - - -def get_stimulus_set(name): - stimulus_set_model = StimulusSetModel.get(StimulusSetModel.name == name) - image_paths = fetch_stimulus_set(stimulus_set_model) - pw_query = ImageModel.select() \ - .join(StimulusSetImageMap) \ - .join(StimulusSetModel) \ - .where(StimulusSetModel.name == name) - df_reconstructed = pd.DataFrame(list(pw_query.dicts())) - pw_query_attributes = AttributeModel.select() \ - .join(ImageMetaModel) \ - .join(ImageModel) \ - .join(StimulusSetImageMap) \ - .join(StimulusSetModel) \ - .where(StimulusSetModel.name == name) \ - .distinct() - for a in pw_query_attributes: - pw_query_single_attribute = AttributeModel.select(ImageModel.image_id, ImageMetaModel.value) \ - .join(ImageMetaModel) \ - .join(ImageModel) \ - .join(StimulusSetImageMap) \ - .join(StimulusSetModel) \ - .where((StimulusSetModel.name == name) & (AttributeModel.name == a.name)) - df_single_attribute = pd.DataFrame(list(pw_query_single_attribute.dicts())) - merged = df_reconstructed.merge(df_single_attribute, on="image_id", how="left", suffixes=("orig_", "")) - df_reconstructed[a.name] = merged["value"].astype(a.type) - stimulus_set = StimulusSet(df_reconstructed) - stimulus_set.image_paths = image_paths - return stimulus_set diff --git a/brainscore/knownfile.db b/brainscore/knownfile.db deleted file mode 100644 index f788517f571980bd2addf9119e81aefde5a30e7e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3756032 zcmeFack~rS*6-bYaz12GL4G1AcvLzEBZw$w#GLI2s00PGjvNuigqSg7#+=8TbwnNW zm_{9SOqj+r=5#+*hk52c&%N(m&%JA%zg}2t_U?h|uIj2?JAA7>-yOE!dg|H7cUl4q=|?p_x+)^AXn$2JEy zhH*Z-yU zu%7L<{r5i(>lr!hKR<2QsP>`R;=mz8)VX742jKz9zn=3iom<|`j$=33di;NW($shu zz3(`F^YN2_N+C9S#m=OiDr_Pu@tY_!3lg3RL 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global _db - if _db is None: - db_file = os.path.join(os.path.dirname(__file__), "knownfile.db") - _db = peewee.SqliteDatabase(db_file) - return _db - - -def hash_a_file(path, buffer_size=64*2**10): - sha1 = hashlib.sha1() - with open(path, "rb") as f: - buffer = f.read(buffer_size) - while len(buffer) > 0: - sha1.update(buffer) - buffer = f.read(buffer_size) - return sha1.hexdigest() - - - -class FileRecord(peewee.Model): - sha1 = peewee.CharField(unique=True) - - class Meta: - database = get_db() - - -class Sighting(peewee.Model): - location = peewee.CharField() - file_record = peewee.ForeignKeyField(FileRecord, backref="sightings") - stamp = peewee.DateTimeField(default=datetime.datetime.now) - - class Meta: - database = get_db() - - -class KnownFile(object): - def __init__(self, path): - self.path = path - self.exists = os.path.exists(path) - if self.exists: - self.realpath = os.path.realpath(path) - self.isfile = os.path.isfile(path) - if self.isfile: - self.sha1 = hash_a_file(path) - self.file_record, fr_created = FileRecord.get_or_create(sha1=self.sha1) - self.sighting, s_created = Sighting.get_or_create(location=self.path, file_record=self.file_record) - - # self.sighting.save() - - -get_db().create_tables([FileRecord, Sighting]) diff --git a/brainscore/lookup.db b/brainscore/lookup.db deleted file mode 100755 index 9c4b7010ebd4a63d4431c488d91bc2973842267b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 49074176 zcmeFaWq1@zx5r!U-jm=?aEAm9?(Qxd3rTP%=ms{`g9Nt_+#Q0uySux)yW9PDb$4dtDNr#%DhvV`Ojx398FLlL4gDX5)?>KAVGly z1riiUP#{5p1O*ZlNKha_f&VuY$UDWf@V?3Hro{KuchR@ox6Bvo8|fS9>*{OmtLH23 z%j~o4-}Za^y1m0*YEQENwmakGg#Qv0NKha_fdmB-6i84YL4gDX5)?>KAVGly1rika zF9iaVi6lefo{_z}b?zP2pw!5t03HEN!4jipC#n+o5x0 ztL|Y@?OXNk+M!2p_fL`qh$Pp)zTB&QSO|{mK9Ts6_iEd@TUeB3izI3CUL6KRGB>G( zk51mR|A1D#BD;ijjp`8Y9X8qFe*gQh;T`V(?`NC3ND{km!v5COnAb1fQ$4Rgd(BWp z5|}*7d&!WQf$x$;aF2$E 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