def graph_definition(edge_string):
"""Parse a graph definition string
>>> graph_definition('Origin,3,9_I_5.6~Origin_II~I_III')
[{'source': 0, 'target': 1, 'value': 5.6}, {'source': 0, 'target': 2, 'value': 4}, {'source': 1, 'target': 3, 'value': 4}]
"""
edge_definitions = listify(edge_string, delim=graph_definition.delim)
edge_list, node_list, node_names = [], [], []
for i, edge_definition in enumerate(edge_definitions):
edge = {}
node_string_pair = listify(edge_definition, delim=graph_definition.node_pair_delim)
for j, field in enumerate(graph_definition.schema):
try:
obj = field['type'](node_string_pair[j])
# if the field holds a node (source or target) then its name must be retrieved
if isinstance(obj, Mapping) and 'name' in obj:
# is this node name already in our list (and other node properties already defined)
if obj['name'] in node_names:
node_index = node_names.index(obj['name'])
# update the existing node with any new properties of this node
node_list[node_index].update(obj) # obj[1] is a node dict
obj = node_index
else:
# reprocess/recast the object (which is now node dict) to add default values to the node
obj = field['type'](obj)
node_list += [obj] # obj[1] is a node dict
node_names += [obj['name']] # obj[1] is a node dict
# since this is a new node, its node index is one less than the len of the node list (or node name list)
obj = len(node_list) - 1
edge[field['key']] = obj
except:
edge[field['key']] = field['default']
edge_list += [edge]
return edge_list, node_list
def lagged_series(series, lags=1, pads=None):
"""
Delay each time series in a set of time series by the lags (number of samples) indicated.
Pad any gaps in the resulting series with the value of pads or clip, if None.
TODO: Allow fractional sample lags (interpolation)
TODO: Allow time value lags instead of sample counts
TODO: Incorporate into the nlp.db.Columns class
>>> lagged_series([[-1, 0, 1, 2, 3], [2, 7, 1, 8, 2], [8, 1, 8, 2, 8]], lags=3)
[[-1, 0, 1, 2, 3], [1, 8, 2, 2, 7], [8, 2, 8, 8, 1]]
>>> lagged_series([[-1, 0, 1, 2, 3], [2, 7, 1, 8, 2], [8, 1, 8, 2, 8]], lags=[2, 1], pads=0)
[[-1, 0, 1, 2, 3], [0, 0, 2, 7, 1], [0, 8, 1, 8, 2]]
>>> lagged_series([[-1, 0, 1, 2, 3], [2, 7, 1, 8, 2], [8, 1, 8, 2, 8]], lags=[-1, 3], pads=[-9, -5])
[[-1, 0, 1, 2, 3], [7, 1, 8, 2, -9], [-5, -5, -5, 8, 1]]
"""
N = len(series) - 1
pads = [None] * N if pads is None else util.listify(pads, N)
pads = [None] + pads
lags = [None] * N if lags is None else util.listify(lags, N)
lags = [None] + lags
ans = [series[0]]
for i in range(1, min(len(lags) + 1, len(pads) + 1, N + 1)):
#print pads[i]
ans += [lagged_seq(series[i], lags[i], pads[i])]
return ans
def lagged_series(series, lags=1, pads=None):
"""
Delay each time series in a set of time series by the lags (number of samples) indicated.
Pad any gaps in the resulting series with the value of pads or clip, if None.
TODO: Allow fractional sample lags (interpolation)
TODO: Allow time value lags instead of sample counts
TODO: Incorporate into the nlp.db.Columns class
>>> lagged_series([[-1, 0, 1, 2, 3], [2, 7, 1, 8, 2], [8, 1, 8, 2, 8]], lags=3)
[[-1, 0, 1, 2, 3], [1, 8, 2, 2, 7], [8, 2, 8, 8, 1]]
>>> lagged_series([[-1, 0, 1, 2, 3], [2, 7, 1, 8, 2], [8, 1, 8, 2, 8]], lags=[2, 1], pads=0)
[[-1, 0, 1, 2, 3], [0, 0, 2, 7, 1], [0, 8, 1, 8, 2]]
>>> lagged_series([[-1, 0, 1, 2, 3], [2, 7, 1, 8, 2], [8, 1, 8, 2, 8]], lags=[-1, 3], pads=[-9, -5])
[[-1, 0, 1, 2, 3], [7, 1, 8, 2, -9], [-5, -5, -5, 8, 1]]
"""
N = len(series) - 1
pads = [None] * N if pads is None else util.listify(pads, N)
pads = [None] + pads
lags = [None] * N if lags is None else util.listify(lags, N)
lags = [None] + lags
ans = [series[0]]
for i in range(1, min(len(lags) + 1, len(pads) + 1, N + 1)):
#print pads[i]
ans += [lagged_seq(series[i], lags[i], pads[i])]
return ans
def make_time_series(x,
t=pd.Timestamp(datetime.datetime(1970, 1, 1)),
freq=None):
"""Convert a 2-D array of time/value pairs (or pair of time/value vectors) into a pd.Series time-series
>>> make_time_series(range(3), freq='15min') # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
1970-01-01 00:00:00 NaN
1970-01-01 00:15:00 NaN
1970-01-01 00:30:00 NaN
dtype: float64
"""
if isinstance(x, pd.DataFrame):
x = pd.Series(x[x.columns[0]])
elif not isinstance(x, pd.Series) and (
not isinstance(t,
(pd.Series, pd.Index, list, tuple)) or not len(t)):
#warnings.warn("Coercing a non-Series")
if len(x) == 2:
t, x = listify(x[0]), listify(x[1])
elif len(x) >= 2:
try:
t, x = zip(*x)
except (ValueError, IndexError, TypeError):
pass
x = pd.Series(x)
else:
if isinstance(t, (datetime.datetime, pd.Timestamp)):
t = pd.Timestamp(t)
else:
x = pd.Series(listify(x), index=listify(t))
if not isinstance(x, pd.Series):
raise TypeError(
"`pug.invest.util.make_time_series(x, t)` expects x to be a type that"
" can be coerced to a Series object, but it's type is: {0}".format(
type(x)))
# By this point x must be a Series, only question is whether its index needs to be converted to a DatetimeIndex
if x.index[0] != 0 and isinstance(
x.index[0], (datetime.date, datetime.datetime, pd.Timestamp,
basestring, float, np.int64, int)):
t = x.index
elif isinstance(t, (datetime.date, datetime.datetime, pd.Timestamp,
basestring, float, np.int64, int)):
if not freq:
freq = '15min'
warnings.warn(
'Assumed time series freq to be {0} though no freq argument was provided!'
.format(freq), RuntimeWarning)
t = pd.date_range(t, periods=len(x), freq=freq)
x = pd.Series(x, index=t)
if isinstance(x, pd.Series):
x.index = pd.DatetimeIndex(x.index.values)
return x
def __init__(self, start_urls=None, *args, **kwargs):
self.start_urls = [
r'http://www.google.com/googlebooks/uspto-patents-applications-biblio.html'
]
if start_urls:
self.start_urls = listify(start_urls)
super(PatentBiblioSpider, self).__init__(*args, **kwargs)
def node_name(name, use_defaults=False):
"""
>>> sorted(node_name('Origin,2.7, 3 ')[1].items())
[('charge', 2.7), ('group', 3), ('name', 'Origin')]
>>> node_name('Origin,2.7, 3 ')[0]
'Origin'
"""
# if the name is not a string, but a dict defining a node, then just set the defaults and return it
if isinstance(name, Mapping):
ans = dict(name)
for j, field in enumerate(node_name.schema):
if field['key'] not in ans:
ans[field['key']] = field['default']
return ans
seq = listify(name, delim=',')
ans = {}
for j, field in enumerate(node_name.schema):
if 'default' in field:
try:
ans[field['key']] = field['type'](seq[j])
except:
if use_defaults:
ans[field['key']] = field['default']
else:
try:
ans[field['key']] = ans.get(field['key'],
field['type'](seq[j]))
except:
pass
return ans
def dropna(x):
"""Delete all NaNs and and infinities in a sequence of real values
Returns:
list: Array of all values in x that are between -inf and +inf, exclusive
"""
return [x_i for x_i in listify(x) if float('-inf') < x_i < float('inf')]
def dropna(x):
"""Delete all NaNs and and infinities in a sequence of real values
Returns:
list: Array of all values in x that are between -inf and +inf, exclusive
"""
return [x_i for x_i in listify(x) if float('-inf') < x_i < float('inf')]
def args_tptnfpfn(*args, **kwargs):
"""Convert kwargs for tp, tn, fp, fn to ordered tuple of args
If a single tuple/list is passed as the first arg, it is assumed to be the desired tuple of args
>>> args_tptnfpfn(1, 2, 3, 4)
(1, 2, 3, 4)
>>> args_tptnfpfn((1, 2, 3, 4))
(1, 2, 3, 4)
>>> args_tptnfpfn([1, 2, 3, 4])
(1, 2, 3, 4)
>>> args_tptnfpfn(3, 4, tp=1, tn=2)
(1, 2, 3, 4)
>>> args_tptnfpfn(tp=1, tn=2)
(1, 2, 0, 0)
>>> args_tptnfpfn(tp=1, tn=2, fp=3, fn=4)
(1, 2, 3, 4)
>>> args_tptnfpfn(1)
(1, 0, 0, 0)
"""
if len(args) == 4:
tp, tn, fp, fn = args
elif len(kwargs) == 0:
if len(args) == 1:
args = listify(args[0])
tp, tn, fp, fn = list(list(args) + [0] * (4 - len(args)))
else:
args = list(args)
tp = kwargs['tp'] if 'tp' in kwargs else args.pop(0) if len(args) else 0
tn = kwargs['tn'] if 'tn' in kwargs else args.pop(0) if len(args) else 0
fp = kwargs['fp'] if 'fp' in kwargs else args.pop(0) if len(args) else 0
fn = kwargs['fn'] if 'fn' in kwargs else args.pop(0) if len(args) else 0
return tp, tn, fp, fn
def append_app_urls(local, app_names):
app_names = listify(app_names) # or local.get('local.settings.INSTALLED_APPS') ;)
urlpatterns = local.get('urlpatterns', patterns(''))
for app_name in app_names:
urlpatterns += patterns('', url(r'^', include('%s.urls' % app_name)))#, name='order-list'),)
local['urlpatterns'] = urlpatterns
def node_name(name, use_defaults=False):
"""
>>> sorted(node_name('Origin,2.7, 3 ')[1].items())
[('charge', 2.7), ('group', 3), ('name', 'Origin')]
>>> node_name('Origin,2.7, 3 ')[0]
'Origin'
"""
# if the name is not a string, but a dict defining a node, then just set the defaults and return it
if isinstance(name, Mapping):
ans = dict(name)
for j, field in enumerate(node_name.schema):
if field['key'] not in ans:
ans[field['key']] = field['default']
return ans
seq = listify(name, delim=',')
ans = {}
for j, field in enumerate(node_name.schema):
if 'default' in field:
try:
ans[field['key']] = field['type'](seq[j])
except:
if use_defaults:
ans[field['key']] = field['default']
else:
try:
ans[field['key']] = ans.get(field['key'], field['type'](seq[j]))
except:
pass
return ans
def augment_model_meta(model, db_alias, model_meta, column_name_filters=None, count=0, verbosity=0):
"""Fields are keyed by their db_column name rather than field name (like model_meta)"""
if settings.DEBUG and verbosity > 2:
print 'Augmenting model meta data for %r...' % model
column_name_filters = util.listify(column_name_filters)
queryset = djdb.get_queryset(model)
if db_alias:
queryset = queryset.using(db_alias)
for field_name in model._meta.get_all_field_names():
field = None
try:
field = model._meta.get_field(field_name)
db_column = field.db_column
# Django creates reverse ForeignKey relationship fields that may not have a database column in this table
# This happens if you make existing fields/columns in other tables a ForeignKey referencing this table
except FieldDoesNotExist:
db_column = None
if not field:
if verbosity:
print "WARNING: Skipped 'phantom' field named '%s'. This is likely because of a ForeignKey relationship elsewhere back to this model (%r). No field found in the model '%s' for database '%s'." % (field_name, model, model.__name__, db_alias)
continue
if not db_column:
if field.name in model_meta:
db_column = field.name
elif field.name.lower() in model_meta:
db_column = field.name.lower()
elif field.name.upper() in model_meta:
db_column = field.name.upper()
if not db_column:
if verbosity:
print "WARNING: Skipped field named '%s'. No column found in the database.table '%s.%s'." % (field.name, db_alias, model.__name__)
continue
if column_name_filters:
if not any(((callable(cnf) and cnf(db_column)) or (db_column == cnf)) for cnf in column_name_filters):
if verbosity:
print "WARNING: Skipped field named '%s' for table '%s.%s' because it didn't match any filters: %r." % (field.name, db_alias, model.__name__, column_name_filters)
continue
if (field.name == 'id' and isinstance(field, models.fields.AutoField)
and field.primary_key and (not model_meta[db_column]['primary_key'])):
print "WARNING: Skipped field named '%s' for table '%s.%s' because it is an AutoField and no primary_key is defined for this table." % (field.name, db_alias, model.__name__)
continue
model_meta[db_column] = augment_field_meta(field, queryset, model_meta[db_column], count=count, verbosity=verbosity)
if verbosity > 1:
print '%s (%s of type %s) has %s / %s (%3.1f%%) distinct values between %s and %s, excluding %s nulls.' % (field.name, db_column,
model_meta[db_column]['type'],
model_meta[db_column]['num_distinct'],
count,
100. * (model_meta[db_column]['num_distinct'] or 0) / (count or 1),
repr(model_meta[db_column]['min']),
repr(model_meta[db_column]['max']),
model_meta[db_column]['num_null'])
return model_meta
def make_time_series(x, t=pd.Timestamp(datetime.datetime(1970,1,1)), freq=None):
"""Convert a 2-D array of time/value pairs (or pair of time/value vectors) into a pd.Series time-series
>>> make_time_series(range(3)) # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
1970-01-01 00:00:00 NaN
1970-01-01 00:15:00 NaN
1970-01-01 00:30:00 NaN
dtype: float64
"""
if isinstance(x, pd.DataFrame):
x = pd.Series(x[x.columns[0]])
elif not isinstance(x, pd.Series) and (not isinstance(t, (pd.Series, pd.Index, list, tuple)) or not len(t)):
#warnings.warn("Coercing a non-Series")
if len(x) == 2:
t, x = listify(x[0]), listify(x[1])
elif len(x) >= 2:
try:
t, x = zip(*x)
except (ValueError, IndexError, TypeError):
pass
x = pd.Series(x)
else:
if isinstance(t, (datetime.datetime, pd.Timestamp)):
t = pd.Timestamp(t)
else:
x = pd.Series(listify(x), index=listify(t))
if not isinstance(x, pd.Series):
raise TypeError("`pug.invest.util.make_time_series(x, t)` expects x to be a type that can be coerced to a Series object, but it's type is: {0}".format(type(x)))
# By this point x must be a Series, only question is whether its index needs to be converted to a DatetimeIndex
if x.index[0] != 0 and isinstance(x.index[0], (datetime.date, datetime.datetime, pd.Timestamp, basestring, float, np.int64, int)):
t = x.index
elif isinstance(t, (datetime.date, datetime.datetime, pd.Timestamp, basestring, float, np.int64, int)):
if not freq:
freq = '15min'
warnings.warn('Assumed time series freq to be {0} though no freq argument was provided!'.format(freq), RuntimeWarning)
t = pd.date_range(t, periods=len(x), freq=freq)
x = pd.Series(x, index=t)
if isinstance(x, pd.Series):
x.index = pd.DatetimeIndex(x.index.values)
return x
def graph_definition(edge_string):
"""Parse a graph definition string
>>> graph_definition('Origin,3,9_I_5.6~Origin_II~I_III')
[{'source': 0, 'target': 1, 'value': 5.6}, {'source': 0, 'target': 2, 'value': 4}, {'source': 1, 'target': 3, 'value': 4}]
"""
edge_definitions = listify(edge_string, delim=graph_definition.delim)
edge_list, node_list, node_names = [], [], []
for i, edge_definition in enumerate(edge_definitions):
edge = {}
node_string_pair = listify(edge_definition,
delim=graph_definition.node_pair_delim)
for j, field in enumerate(graph_definition.schema):
try:
obj = field['type'](node_string_pair[j])
# if the field holds a node (source or target) then its name must be retrieved
if isinstance(obj, Mapping) and 'name' in obj:
# is this node name already in our list (and other node properties already defined)
if obj['name'] in node_names:
node_index = node_names.index(obj['name'])
# update the existing node with any new properties of this node
node_list[node_index].update(
obj) # obj[1] is a node dict
obj = node_index
else:
# reprocess/recast the object (which is now node dict) to add default values to the node
obj = field['type'](obj)
node_list += [obj] # obj[1] is a node dict
node_names += [obj['name']] # obj[1] is a node dict
# since this is a new node, its node index is one less than the len of the node list (or node name list)
obj = len(node_list) - 1
edge[field['key']] = obj
except:
edge[field['key']] = field['default']
edge_list += [edge]
return edge_list, node_list
def append_urls(local, app_names=None):
app_names = listify(app_names or basename(dirname(local.get('__file__', None))))
urlpatterns = local.get('urlpatterns', patterns(''))
for app_name in app_names:
print 'Composing REST API URLs (Django urlconf entries) for app named %r' % app_name
views_name = app_name + '.views'
app_module = __import__(views_name)
app = get_app(app_name)
for Model in get_models(app):
# print Model
model_name = Model.__name__
View = app_module.views.__dict__[model_name + 'List']
urlpatterns += patterns('', url(r'^' + app_name + r'/' + model_name, View.as_view()))#, name='order-list'),)
local['urlpatterns'] = urlpatterns
def create_model_viewsets(local, app_names=None):
app_names = listify(
app_names
or os.path.basename(os.path.dirname(local.get('__file__', None))))
for app_name in app_names: # , 'npc_s'):
app = get_app(app_name)
for Model in get_models(app):
class KitchenSinkFilter(more_django_filters.FilterSet):
class Meta:
model = Model
# fields = tuple(f.name for f in model._meta.fields)
# KitchenSinkFilter.__doc__ = "Filter (query) for records the database.table %s.%s\n%s" % (app_name, Model.__name__, Model.__doc__)
class KitchenSinkSerializer(serializers.ModelSerializer):
class Meta:
model = KitchenSinkFilter.Meta.model
class KitchenSinkList(generics.ListAPIView):
__doc__ = "Filtered list of database records (table rows) for the database.table <strong>%s.%s</strong>\n<br>\n%s" % (
app_name, Model.__name__, Model.__doc__)
model = KitchenSinkFilter.Meta.model
serializer_class = KitchenSinkSerializer
#filter_fields = ('acctno','whse','status','partno','date_time','reference','return_days')
filter_class = KitchenSinkFilter
class Meta:
model = Model
fields = tuple(f.name for f in model._meta.fields)
KitchenSinkList.__name__ = Model.__name__ + 'List'
# KitchenSinkList.__doc__ = "Filtered list of database records (table rows) for the database.table %s.%s\n%s" % (app_name, Model.__name__, Model.__doc__)
local[KitchenSinkList.__name__] = KitchenSinkList
class KitchenSinkViewSet(viewsets.ModelViewSet):
serializer_class = KitchenSinkSerializer
model = KitchenSinkFilter.Meta.model
filter_fields = tuple(f.name for f in model._meta.fields)
order_by = tuple(f.name for f in model._meta.fields)
KitchenSinkViewSet.__name__ = Model.__name__ + 'ViewSet'
# KitchenSinkViewSet.__doc__ = "A ViewSet for the database.table %s.%s\n%s" % (app_name, Model.__name__, Model.__doc__)
local[KitchenSinkViewSet.__name__] = KitchenSinkViewSet
def create_model_viewsets(local, app_names=None):
app_names = listify(app_names or os.path.basename(os.path.dirname(local.get('__file__', None))))
for app_name in app_names: # , 'npc_s'):
app = get_app(app_name)
for Model in get_models(app):
class KitchenSinkFilter(more_django_filters.FilterSet):
class Meta:
model = Model
# fields = tuple(f.name for f in model._meta.fields)
# KitchenSinkFilter.__doc__ = "Filter (query) for records the database.table %s.%s\n%s" % (app_name, Model.__name__, Model.__doc__)
class KitchenSinkSerializer(serializers.ModelSerializer):
class Meta:
model = KitchenSinkFilter.Meta.model
class KitchenSinkList(generics.ListAPIView):
__doc__ = "Filtered list of database records (table rows) for the database.table <strong>%s.%s</strong>\n<br>\n%s" % (app_name, Model.__name__, Model.__doc__)
model = KitchenSinkFilter.Meta.model
serializer_class = KitchenSinkSerializer
#filter_fields = ('acctno','whse','status','partno','date_time','reference','return_days')
filter_class = KitchenSinkFilter
class Meta:
model = Model
fields = tuple(f.name for f in model._meta.fields)
KitchenSinkList.__name__ = Model.__name__ + 'List'
# KitchenSinkList.__doc__ = "Filtered list of database records (table rows) for the database.table %s.%s\n%s" % (app_name, Model.__name__, Model.__doc__)
local[KitchenSinkList.__name__] = KitchenSinkList
class KitchenSinkViewSet(viewsets.ModelViewSet):
serializer_class = KitchenSinkSerializer
model = KitchenSinkFilter.Meta.model
filter_fields = tuple(f.name for f in model._meta.fields)
order_by = tuple(f.name for f in model._meta.fields)
KitchenSinkViewSet.__name__ = Model.__name__ + 'ViewSet'
# KitchenSinkViewSet.__doc__ = "A ViewSet for the database.table %s.%s\n%s" % (app_name, Model.__name__, Model.__doc__)
local[KitchenSinkViewSet.__name__] = KitchenSinkViewSet
def animate_panel(panel, keys=None, columns=None, interval=1000, blit=False, titles='', path='animate_panel', xlabel='Time', ylabel='Value', ext='gif',
replot=False, linewidth=3, close=False, fontsize=24, background_color='white', alpha=1, figsize=(12,8), xlabel_rotation=-25, plot_kwargs=(('rotation', 30),),
verbosity=1, **video_kwargs):
"""Animate a pandas.Panel by flipping through plots of the data in each dataframe
Arguments:
panel (pandas.Panel): Pandas Panel of DataFrames to animate (each DataFrame is an animation video frame)
keys (list of str): ordered list of panel keys (pages) to animate
columns (list of str): ordered list of data series names to include in plot for eath video frame
interval (int): number of milliseconds between video frames
titles (str or list of str): titles to place in plot on each data frame.
default = `keys` so that titles changes with each frame
path (str): path and base file name to save *.mp4 animation video ('' to not save)
kwargs (dict): pass-through kwargs for `animation.FuncAnimation(...).save(path, **kwargs)`
(Not used if `not path`)
TODO:
- Work with other 3-D data formats:
- dict (sorted by key) or OrderedDict
- list of 2-D arrays/lists
- 3-D arrays/lists
- generators of 2-D arrays/lists
- generators of generators of lists/arrays?
- Write json and html5 files for d3 SVG line plots with transitions!
>>> x = np.arange(0, 2*np.pi, 0.05)
>>> panel = pd.Panel(dict((i, pd.DataFrame({
... 'T=10': np.sin(x + i/10.),
... 'T=7': np.sin(x + i/7.),
... 'beat': np.sin(x + i/10.) + np.sin(x + i/7.),
... }, index=x)
... ) for i in range(50)))
>>> animate_panel(panel, interval=200, path='animate_panel_test') # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
Drawing frames for a ".gif" animation...
Saving video to animate_panel_test.gif...
T=10 T=7 beat
0.00 0.000000 0.000000 0.000000
0.05 0.049979 0.049979 0.099958
...
[126 rows x 3 columns]
"""
plot_kwargs = plot_kwargs or {}
plot_kwargs = dict(plot_kwargs)
ext_kwargs = {
'mp4': {'writer': 'ffmpeg', 'codec': 'mpeg4', 'dpi': 100, 'bitrate': 2000},
'gif': {'writer': 'imagemagick', 'dpi': 100, 'bitrate': 2000},
'imagemagic.gif': {'writer': 'imagemagick_gif', 'dpi': 100, 'bitrate': 2000},
}
ext = str(ext).lower().strip() or 'gif'
default_kwargs = ext_kwargs.get(ext, {})
keys = keys or list(panel.keys())
if titles:
titles = listify(titles)
if len(titles) == 1:
titles *= len(keys)
else:
titles = keys
titles = dict((k, title) for k, title in zip(keys, titles))
columns = columns or list(panel[keys[0]].columns)
fig, ax = plt.subplots(figsize=figsize)
fig.patch.set_facecolor(background_color)
fig.patch.set_alpha(alpha)
i = 0
df = panel[keys[i]]
x = df.index.values
y = df[columns].values
lines = ax.plot(x, y)
ax.grid('on')
ax.patch.set_facecolor(background_color)
ax.patch.set_alpha(alpha)
ax.title.set_text(titles[keys[0]])
ax.title.set_fontsize(fontsize)
ax.title.set_fontweight('bold')
ax.xaxis.label.set_text(xlabel)
plt.setp(ax.get_xticklabels(), rotation=xlabel_rotation)
ax.yaxis.label.set_text(ylabel)
ax.legend(columns)
def animate(k):
df = panel[k]
x = df.index.values
y = df[columns].values.T
if replot:
# plt.cla()
# fig, ax = plt.subplots(figsize=figsize)
fig = ax.figure
fig.patch.set_facecolor(background_color)
fig.patch.set_alpha(alpha)
lines = ax.plot(x, y.T, linewidth=linewidth)
ax.grid('on')
ax.patch.set_facecolor(background_color)
ax.patch.set_alpha(alpha)
ax.title.set_text(titles[k])
ax.title.set_fontsize(fontsize)
ax.title.set_fontweight('bold')
ax.xaxis.label.set_text(xlabel)
plt.setp(ax.get_xticklabels(), rotation=xlabel_rotation)
ax.yaxis.label.set_text(ylabel)
ax.legend(columns)
else:
lines = ax.lines
fig = ax.figure
for i in range(len(lines)):
lines[i].set_xdata(x) # all lines have to share the same x-data
lines[i].set_ydata(y[i]) # update the data, don't replot a new line
lines[i].set_linewidth(linewidth)
lines[i].figure.set_facecolor(background_color)
lines[i].figure.set_alpha(alpha)
lines[i].axes.patch.set_facecolor(background_color)
lines[i].axes.patch.set_alpha(alpha)
ax.patch.set_facecolor(background_color)
ax.figure.patch.set_alpha(alpha)
ax.title.set_text(titles[k])
ax.title.set_fontsize(fontsize)
ax.title.set_fontweight('bold')
if blit:
return lines
# FIXME: doesn't work with ext=mp4
# init_func to mask out pixels to be redrawn/cleared which speeds redrawing of plot
def mask_lines():
if verbosity > 0:
print('Initialing mask_lines. . .')
df = panel[0]
x = df.index.values
y = df[columns].values.T
for i in range(len(lines)):
# FIXME: why are x-values used to set the y-data coordinates of the mask?
lines[i].set_xdata(np.ma.array(x, mask=True))
lines[i].set_ydata(np.ma.array(y[i], mask=True))
return lines
if verbosity > 0:
print('Drawing frames for a ".{0}" animation{1}. . .'.format(ext, ' with blitting' if blit else ''))
animate(keys[0])
ani = animation.FuncAnimation(fig, animate, keys, interval=interval, blit=blit) #, init_func=mask_lines, blit=True)
kwargs = dict(default_kwargs)
for k, v in six.iteritems(default_kwargs):
kwargs[k] = video_kwargs.get(k, v)
# if 'bitrate' in kwargs:
# kwargs['bitrate'] = min(kwargs['bitrate'], int(8e5 / interval)) # low information rate (long interval) might make it impossible to achieve a higher bitrate ight not
if path and isinstance(path, basestring):
path += '.{0}'.format(ext)
if verbosity > 0:
print('Saving video to {0}. . .'.format(path))
ani.save(path, **kwargs)
if close:
plt.close(fig)
return df
def animate_panel(panel,
keys=None,
columns=None,
interval=1000,
blit=False,
titles='',
path='animate_panel',
xlabel='Time',
ylabel='Value',
ext='gif',
replot=False,
linewidth=3,
close=False,
fontsize=24,
background_color='white',
alpha=1,
figsize=(12, 8),
xlabel_rotation=-25,
plot_kwargs=(('rotation', 30), ),
verbosity=1,
**video_kwargs):
"""Animate a pandas.Panel by flipping through plots of the data in each dataframe
Arguments:
panel (pandas.Panel): Pandas Panel of DataFrames to animate (each DataFrame is an animation video frame)
keys (list of str): ordered list of panel keys (pages) to animate
columns (list of str): ordered list of data series names to include in plot for eath video frame
interval (int): number of milliseconds between video frames
titles (str or list of str): titles to place in plot on each data frame.
default = `keys` so that titles changes with each frame
path (str): path and base file name to save *.mp4 animation video ('' to not save)
kwargs (dict): pass-through kwargs for `animation.FuncAnimation(...).save(path, **kwargs)`
(Not used if `not path`)
TODO:
- Work with other 3-D data formats:
- dict (sorted by key) or OrderedDict
- list of 2-D arrays/lists
- 3-D arrays/lists
- generators of 2-D arrays/lists
- generators of generators of lists/arrays?
- Write json and html5 files for d3 SVG line plots with transitions!
>>> x = np.arange(0, 2*np.pi, 0.05)
>>> panel = pd.Panel(dict((i, pd.DataFrame({
... 'T=10': np.sin(x + i/10.),
... 'T=7': np.sin(x + i/7.),
... 'beat': np.sin(x + i/10.) + np.sin(x + i/7.),
... }, index=x)
... ) for i in range(50)))
>>> animate_panel(panel, interval=200, path='animate_panel_test') # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
Drawing frames for a ".gif" animation...
Saving video to animate_panel_test.gif...
T=10 T=7 beat
0.00 0.000000 0.000000 0.000000
0.05 0.049979 0.049979 0.099958
...
[126 rows x 3 columns]
"""
plot_kwargs = plot_kwargs or {}
plot_kwargs = dict(plot_kwargs)
ext_kwargs = {
'mp4': {
'writer': 'ffmpeg',
'codec': 'mpeg4',
'dpi': 100,
'bitrate': 2000
},
'gif': {
'writer': 'imagemagick',
'dpi': 100,
'bitrate': 2000
},
'imagemagic.gif': {
'writer': 'imagemagick_gif',
'dpi': 100,
'bitrate': 2000
},
}
ext = str(ext).lower().strip() or 'gif'
default_kwargs = ext_kwargs.get(ext, {})
keys = keys or list(panel.keys())
if titles:
titles = listify(titles)
if len(titles) == 1:
titles *= len(keys)
else:
titles = keys
titles = dict((k, title) for k, title in zip(keys, titles))
columns = columns or list(panel[keys[0]].columns)
fig, ax = plt.subplots(figsize=figsize)
fig.patch.set_facecolor(background_color)
fig.patch.set_alpha(alpha)
i = 0
df = panel[keys[i]]
x = df.index.values
y = df[columns].values
lines = ax.plot(x, y)
ax.grid('on')
ax.patch.set_facecolor(background_color)
ax.patch.set_alpha(alpha)
ax.title.set_text(titles[keys[0]])
ax.title.set_fontsize(fontsize)
ax.title.set_fontweight('bold')
ax.xaxis.label.set_text(xlabel)
plt.setp(ax.get_xticklabels(), rotation=xlabel_rotation)
ax.yaxis.label.set_text(ylabel)
ax.legend(columns)
def animate(k):
df = panel[k]
x = df.index.values
y = df[columns].values.T
if replot:
# plt.cla()
# fig, ax = plt.subplots(figsize=figsize)
fig = ax.figure
fig.patch.set_facecolor(background_color)
fig.patch.set_alpha(alpha)
lines = ax.plot(x, y.T, linewidth=linewidth)
ax.grid('on')
ax.patch.set_facecolor(background_color)
ax.patch.set_alpha(alpha)
ax.title.set_text(titles[k])
ax.title.set_fontsize(fontsize)
ax.title.set_fontweight('bold')
ax.xaxis.label.set_text(xlabel)
plt.setp(ax.get_xticklabels(), rotation=xlabel_rotation)
ax.yaxis.label.set_text(ylabel)
ax.legend(columns)
else:
lines = ax.lines
fig = ax.figure
for i in range(len(lines)):
lines[i].set_xdata(
x) # all lines have to share the same x-data
lines[i].set_ydata(
y[i]) # update the data, don't replot a new line
lines[i].set_linewidth(linewidth)
lines[i].figure.set_facecolor(background_color)
lines[i].figure.set_alpha(alpha)
lines[i].axes.patch.set_facecolor(background_color)
lines[i].axes.patch.set_alpha(alpha)
ax.patch.set_facecolor(background_color)
ax.figure.patch.set_alpha(alpha)
ax.title.set_text(titles[k])
ax.title.set_fontsize(fontsize)
ax.title.set_fontweight('bold')
if blit:
return lines
# FIXME: doesn't work with ext=mp4
# init_func to mask out pixels to be redrawn/cleared which speeds redrawing of plot
def mask_lines():
if verbosity > 0:
print('Initialing mask_lines. . .')
df = panel[0]
x = df.index.values
y = df[columns].values.T
for i in range(len(lines)):
# FIXME: why are x-values used to set the y-data coordinates of the mask?
lines[i].set_xdata(np.ma.array(x, mask=True))
lines[i].set_ydata(np.ma.array(y[i], mask=True))
return lines
if verbosity > 0:
print('Drawing frames for a ".{0}" animation{1}. . .'.format(
ext, ' with blitting' if blit else ''))
animate(keys[0])
ani = animation.FuncAnimation(
fig, animate, keys, interval=interval,
blit=blit) #, init_func=mask_lines, blit=True)
kwargs = dict(default_kwargs)
for k, v in six.iteritems(default_kwargs):
kwargs[k] = video_kwargs.get(k, v)
# if 'bitrate' in kwargs:
# kwargs['bitrate'] = min(kwargs['bitrate'], int(8e5 / interval)) # low information rate (long interval) might make it impossible to achieve a higher bitrate ight not
if path and isinstance(path, basestring):
path += '.{0}'.format(ext)
if verbosity > 0:
print('Saving video to {0}. . .'.format(path))
ani.save(path, **kwargs)
if close:
plt.close(fig)
return df
def index_model_field_batches(model_or_queryset, key_fields=['model_number', 'serial_number'], value_fields=['pk'],
key_formatter=lambda x: str.lstrip(str.strip(str(x or '')), '0'),
value_formatter=lambda x: str.strip(str(x)), batch_len=10000,
limit=100000000, verbosity=1):
'''Like index_model_field except uses 50x less memory and 10x more processing cycles
Returns 2 dicts where both the keys and values are tuples:
target_index = {(<key_fields[0]>, <key_fields[1]>, ...): (<value_fields[0]>,)} for all distinct model-serial pairs in the Sales queryset
target_dupes = {(<key_fields[0]>, <key_fields[1]>, ...): [(<value_fields[1]>,), (<value_fields[2]>,), ...]} with all the duplicates except the first pk already listed above
'''
qs = djdb.get_queryset(model_or_queryset)
N = qs.count()
if verbosity:
print 'Indexing %d rows (database records) to aid in finding record %r values using the field %r.' % (N, value_fields, key_fields)
index, dupes, rownum = {}, {}, 0
pbar, rownum = None, 0
if verbosity and N > min(1000000, max(0, 100000**(1./verbosity))):
widgets = [pb.Counter(), '/%d rows: ' % N, pb.Percentage(), ' ', pb.RotatingMarker(), ' ', pb.Bar(),' ', pb.ETA()]
pbar = pb.ProgressBar(widgets=widgets, maxval=N).start()
# to determine the type of the field value and decide whether to strip() or normalize in any way
#obj0 = qs.filter(**{field + '__isnull': False}).all()[0]
value_fields = util.listify(value_fields)
key_fields = util.listify(key_fields)
for batch in djdb.generate_queryset_batches(qs, batch_len=batch_len, verbosity=verbosity):
for obj in batch:
# print obj
# normalize the key
keys = []
for kf in key_fields:
k = getattr(obj, kf)
keys += [key_formatter(k or '')]
values = []
keys = tuple(keys)
for vf in value_fields:
v = getattr(obj, vf)
values += [value_formatter(v or '')]
values = tuple(values)
if keys in index:
dupes[keys] = dupes.get(keys, []) + [values]
else:
index[keys] = values
# print rownum / float(N)
if pbar:
pbar.update(rownum)
rownum += 1
if rownum >= limit:
break
if pbar:
pbar.finish()
if verbosity:
print 'Found %d duplicate %s values among the %d records or %g%%' % (len(dupes), key_fields, len(index), len(dupes)*100./(len(index) or 1.))
return index, dupes
def simulate(t=1000, poly=(0., ), sinusoids=None, sigma=0, rw=0, irw=0, rrw=0):
"""Simulate a random signal with seasonal (sinusoids), linear and quadratic trend, RW, IRW, and RRW
Arguments:
t (int or list of float): number of samples or time vector, default = 1000
poly (list of float): polynomial coefficients (in decreasing "order") passed to `numpy.polyval`
i.e. poly[0]*x**(N-1) + ... + poly[N-1]
sinusoids (list of list): [[period], [amplitude, period], or [ampl., period, phase]]
>>> len(simulate(poly=(0,),rrw=1))
1000
>>> simulate(t=range(3), poly=(1,2)) # doctest: +NORMALIZE_WHITESPACE
0 2
1 3
2 4
dtype: float64
>>> all(simulate(t=50, sinusoids=((1,2,3),)) == simulate(t=range(50), sinusoids=((1,2,3),)))
True
>>> any(simulate(t=100))
False
>>> abs(simulate(sinusoids=42.42).values[1] + simulate(sinusoids=42.42).values[-1]) < 1e-10
True
>>> simulate(t=17,sinusoids=[42, 16]).min()
-42.0
>>> all((simulate(t=range(10), sinusoids=(1, 9, 4.5))+simulate(t=10, sinusoids=(1,9))).abs() < 1e-10)
True
"""
if t and isinstance(t, int):
t = np.arange(t, dtype=np.float64)
else:
t = np.array(t, dtype=np.float64)
N = len(t)
poly = poly or (0., )
poly = listify(poly)
y = np.polyval(poly, t)
sinusoids = listify(sinusoids or [])
if any(isinstance(ATP, (int, float)) for ATP in sinusoids):
sinusoids = [sinusoids]
for ATP in sinusoids:
# default period is 1 more than the length of the simulated series (no values of the cycle are repeated)
T = (t[-1] - t[0]) * N / (N - 1.)
# default amplitude is 1 and phase is 0
A, P = 1., 0
try:
A, T, P = ATP
except (TypeError, ValueError):
try:
A, T = ATP
except (TypeError, ValueError):
# default period is 1 more than the length of the simulated series
# (no values of the cycle are repeated)
A = ATP[0]
# print(A, T, P)
# print(t[1] - t[0])
y += A * np.sin(2 * np.pi * (t - P) / T)
if sigma:
y += np.random.normal(0.0, float(sigma), N)
if rw:
y += np.random.normal(0.0, float(rw), N).cumsum()
if irw:
y += np.random.normal(0.0, float(irw), N).cumsum().cumsum()
if rrw:
y += np.random.normal(0.0, float(rrw), N).cumsum().cumsum().cumsum()
return pd.Series(y, index=t)
def __init__(self, start_urls=None, *args, **kwargs):
self.start_urls = [r'http://www.google.com/googlebooks/uspto-patents-applications-biblio.html']
if start_urls:
self.start_urls = listify(start_urls)
super(PatentBiblioSpider, self).__init__(*args, **kwargs)
def simulate(t=1000, poly=(0.,), sinusoids=None, sigma=0, rw=0, irw=0, rrw=0):
"""Simulate a random signal with seasonal (sinusoids), linear and quadratic trend, RW, IRW, and RRW
Arguments:
t (int or list of float): number of samples or time vector, default = 1000
poly (list of float): polynomial coefficients (in decreasing "order") passed to `numpy.polyval`
i.e. poly[0]*x**(N-1) + ... + poly[N-1]
sinusoids (list of list): [[period], [amplitude, period], or [ampl., period, phase]]
>>> len(simulate(poly=(0,),rrw=1))
1000
>>> simulate(t=range(3), poly=(1,2)) # doctest: +NORMALIZE_WHITESPACE
0 2
1 3
2 4
dtype: float64
>>> all(simulate(t=50, sinusoids=((1,2,3),)) == simulate(t=range(50), sinusoids=((1,2,3),)))
True
>>> any(simulate(t=100))
False
>>> abs(simulate(sinusoids=42.42).values[1] + simulate(sinusoids=42.42).values[-1]) < 1e-10
True
>>> simulate(t=17,sinusoids=[42, 16]).min()
-42.0
>>> all((simulate(t=range(10), sinusoids=(1, 9, 4.5))+simulate(t=10, sinusoids=(1,9))).abs() < 1e-10)
True
"""
if t and isinstance(t, int):
t = np.arange(t, dtype=np.float64)
else:
t = np.array(t, dtype=np.float64)
N = len(t)
poly = poly or (0.,)
poly = listify(poly)
y = np.polyval(poly, t)
sinusoids = listify(sinusoids or [])
if any(isinstance(ATP, (int, float)) for ATP in sinusoids):
sinusoids = [sinusoids]
for ATP in sinusoids:
# default period is 1 more than the length of the simulated series (no values of the cycle are repeated)
T = (t[-1] - t[0]) * N / (N - 1.)
# default amplitude is 1 and phase is 0
A, P = 1., 0
try:
A, T, P = ATP
except (TypeError, ValueError):
try:
A, T = ATP
except (TypeError, ValueError):
# default period is 1 more than the length of the simulated series (no values of the cycle are repeated)
A = ATP[0]
# print(A, T, P)
# print(t[1] - t[0])
y += A * np.sin(2 * np.pi * (t - P) / T)
if sigma:
y += np.random.normal(0.0, float(sigma), N)
if rw:
y += np.random.normal(0.0, float(rw), N).cumsum()
if irw:
y += np.random.normal(0.0, float(irw), N).cumsum().cumsum()
if rrw:
y += np.random.normal(0.0, float(rrw), N).cumsum().cumsum().cumsum()
return pd.Series(y, index=t)
