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stringlengths 52
3.87M
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stringclasses 6
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stringlengths 52
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stringlengths 1
47.2k
| func_code_url
stringlengths 85
339
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|---|---|---|---|---|---|---|---|
bukun/TorCMS | torcms/script/tmplchecker/__init__.py | do_for_dir | def do_for_dir(inws, begin):
'''
do something in the directory.
'''
inws = os.path.abspath(inws)
for wroot, wdirs, wfiles in os.walk(inws):
for wfile in wfiles:
if wfile.endswith('.html'):
if 'autogen' in wroot:
continue
check_html(os.path.abspath(os.path.join(wroot, wfile)), begin) | python | def do_for_dir(inws, begin):
'''
do something in the directory.
'''
inws = os.path.abspath(inws)
for wroot, wdirs, wfiles in os.walk(inws):
for wfile in wfiles:
if wfile.endswith('.html'):
if 'autogen' in wroot:
continue
check_html(os.path.abspath(os.path.join(wroot, wfile)), begin) | do something in the directory. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/tmplchecker/__init__.py#L99-L109 |
bukun/TorCMS | torcms/script/tmplchecker/__init__.py | run_checkit | def run_checkit(srws=None):
'''
do check it.
'''
begin = len(os.path.abspath('templates')) + 1
inws = os.path.abspath(os.getcwd())
if srws:
do_for_dir(srws[0], begin)
else:
do_for_dir(os.path.join(inws, 'templates'), begin)
DOT_OBJ.render('xxtmpl', view=True) | python | def run_checkit(srws=None):
'''
do check it.
'''
begin = len(os.path.abspath('templates')) + 1
inws = os.path.abspath(os.getcwd())
if srws:
do_for_dir(srws[0], begin)
else:
do_for_dir(os.path.join(inws, 'templates'), begin)
DOT_OBJ.render('xxtmpl', view=True) | do check it. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/tmplchecker/__init__.py#L112-L123 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.query_all | def query_all():
'''
Query all the records from TabPost2Tag.
'''
recs = TabPost2Tag.select(
TabPost2Tag,
TabTag.kind.alias('tag_kind'),
).join(
TabTag,
on=(TabPost2Tag.tag_id == TabTag.uid)
)
return recs | python | def query_all():
'''
Query all the records from TabPost2Tag.
'''
recs = TabPost2Tag.select(
TabPost2Tag,
TabTag.kind.alias('tag_kind'),
).join(
TabTag,
on=(TabPost2Tag.tag_id == TabTag.uid)
)
return recs | Query all the records from TabPost2Tag. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L21-L32 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.remove_relation | def remove_relation(post_id, tag_id):
'''
Delete the record of post 2 tag.
'''
entry = TabPost2Tag.delete().where(
(TabPost2Tag.post_id == post_id) &
(TabPost2Tag.tag_id == tag_id)
)
entry.execute()
MCategory.update_count(tag_id) | python | def remove_relation(post_id, tag_id):
'''
Delete the record of post 2 tag.
'''
entry = TabPost2Tag.delete().where(
(TabPost2Tag.post_id == post_id) &
(TabPost2Tag.tag_id == tag_id)
)
entry.execute()
MCategory.update_count(tag_id) | Delete the record of post 2 tag. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L35-L44 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.remove_tag | def remove_tag(tag_id):
'''
Delete the records of certain tag.
'''
entry = TabPost2Tag.delete().where(
TabPost2Tag.tag_id == tag_id
)
entry.execute() | python | def remove_tag(tag_id):
'''
Delete the records of certain tag.
'''
entry = TabPost2Tag.delete().where(
TabPost2Tag.tag_id == tag_id
)
entry.execute() | Delete the records of certain tag. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L47-L54 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.query_by_post | def query_by_post(postid):
'''
Query records by post.
'''
return TabPost2Tag.select().where(
TabPost2Tag.post_id == postid
).order_by(TabPost2Tag.order) | python | def query_by_post(postid):
'''
Query records by post.
'''
return TabPost2Tag.select().where(
TabPost2Tag.post_id == postid
).order_by(TabPost2Tag.order) | Query records by post. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L78-L84 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.__get_by_info | def __get_by_info(post_id, catalog_id):
'''
Geo the record by post and catalog.
'''
recs = TabPost2Tag.select().where(
(TabPost2Tag.post_id == post_id) &
(TabPost2Tag.tag_id == catalog_id)
)
if recs.count() == 1:
return recs.get()
elif recs.count() > 1:
# return the first one, and delete others.
out_rec = None
for rec in recs:
if out_rec:
entry = TabPost2Tag.delete().where(
TabPost2Tag.uid == rec.uid
)
entry.execute()
else:
out_rec = rec
return out_rec
return None | python | def __get_by_info(post_id, catalog_id):
'''
Geo the record by post and catalog.
'''
recs = TabPost2Tag.select().where(
(TabPost2Tag.post_id == post_id) &
(TabPost2Tag.tag_id == catalog_id)
)
if recs.count() == 1:
return recs.get()
elif recs.count() > 1:
# return the first one, and delete others.
out_rec = None
for rec in recs:
if out_rec:
entry = TabPost2Tag.delete().where(
TabPost2Tag.uid == rec.uid
)
entry.execute()
else:
out_rec = rec
return out_rec
return None | Geo the record by post and catalog. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L87-L110 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.query_count | def query_count():
'''
The count of post2tag.
'''
recs = TabPost2Tag.select(
TabPost2Tag.tag_id,
peewee.fn.COUNT(TabPost2Tag.tag_id).alias('num')
).group_by(
TabPost2Tag.tag_id
)
return recs | python | def query_count():
'''
The count of post2tag.
'''
recs = TabPost2Tag.select(
TabPost2Tag.tag_id,
peewee.fn.COUNT(TabPost2Tag.tag_id).alias('num')
).group_by(
TabPost2Tag.tag_id
)
return recs | The count of post2tag. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L113-L123 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.update_field | def update_field(uid, post_id=None, tag_id=None, par_id=None):
'''
Update the field of post2tag.
'''
if post_id:
entry = TabPost2Tag.update(
post_id=post_id
).where(TabPost2Tag.uid == uid)
entry.execute()
if tag_id:
entry2 = TabPost2Tag.update(
par_id=tag_id[:2] + '00',
tag_id=tag_id,
).where(TabPost2Tag.uid == uid)
entry2.execute()
if par_id:
entry2 = TabPost2Tag.update(
par_id=par_id
).where(TabPost2Tag.uid == uid)
entry2.execute() | python | def update_field(uid, post_id=None, tag_id=None, par_id=None):
'''
Update the field of post2tag.
'''
if post_id:
entry = TabPost2Tag.update(
post_id=post_id
).where(TabPost2Tag.uid == uid)
entry.execute()
if tag_id:
entry2 = TabPost2Tag.update(
par_id=tag_id[:2] + '00',
tag_id=tag_id,
).where(TabPost2Tag.uid == uid)
entry2.execute()
if par_id:
entry2 = TabPost2Tag.update(
par_id=par_id
).where(TabPost2Tag.uid == uid)
entry2.execute() | Update the field of post2tag. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L126-L146 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.add_record | def add_record(post_id, catalog_id, order=0):
'''
Create the record of post 2 tag, and update the count in g_tag.
'''
rec = MPost2Catalog.__get_by_info(post_id, catalog_id)
if rec:
entry = TabPost2Tag.update(
order=order,
# For migration. the value should be added when created.
par_id=rec.tag_id[:2] + '00',
).where(TabPost2Tag.uid == rec.uid)
entry.execute()
else:
TabPost2Tag.create(
uid=tools.get_uuid(),
par_id=catalog_id[:2] + '00',
post_id=post_id,
tag_id=catalog_id,
order=order,
)
MCategory.update_count(catalog_id) | python | def add_record(post_id, catalog_id, order=0):
'''
Create the record of post 2 tag, and update the count in g_tag.
'''
rec = MPost2Catalog.__get_by_info(post_id, catalog_id)
if rec:
entry = TabPost2Tag.update(
order=order,
# For migration. the value should be added when created.
par_id=rec.tag_id[:2] + '00',
).where(TabPost2Tag.uid == rec.uid)
entry.execute()
else:
TabPost2Tag.create(
uid=tools.get_uuid(),
par_id=catalog_id[:2] + '00',
post_id=post_id,
tag_id=catalog_id,
order=order,
)
MCategory.update_count(catalog_id) | Create the record of post 2 tag, and update the count in g_tag. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L149-L171 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.count_of_certain_category | def count_of_certain_category(cat_id, tag=''):
'''
Get the count of certain category.
'''
if cat_id.endswith('00'):
# The first level category, using the code bellow.
cat_con = TabPost2Tag.par_id == cat_id
else:
cat_con = TabPost2Tag.tag_id == cat_id
if tag:
condition = {
'def_tag_arr': [tag]
}
recs = TabPost2Tag.select().join(
TabPost,
on=((TabPost2Tag.post_id == TabPost.uid) & (TabPost.valid == 1))
).where(
cat_con & TabPost.extinfo.contains(condition)
)
else:
recs = TabPost2Tag.select().where(
cat_con
)
return recs.count() | python | def count_of_certain_category(cat_id, tag=''):
'''
Get the count of certain category.
'''
if cat_id.endswith('00'):
# The first level category, using the code bellow.
cat_con = TabPost2Tag.par_id == cat_id
else:
cat_con = TabPost2Tag.tag_id == cat_id
if tag:
condition = {
'def_tag_arr': [tag]
}
recs = TabPost2Tag.select().join(
TabPost,
on=((TabPost2Tag.post_id == TabPost.uid) & (TabPost.valid == 1))
).where(
cat_con & TabPost.extinfo.contains(condition)
)
else:
recs = TabPost2Tag.select().where(
cat_con
)
return recs.count() | Get the count of certain category. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L174-L200 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.query_pager_by_slug | def query_pager_by_slug(slug, current_page_num=1, tag='', order=False):
'''
Query pager via category slug.
'''
cat_rec = MCategory.get_by_slug(slug)
if cat_rec:
cat_id = cat_rec.uid
else:
return None
# The flowing code is valid.
if cat_id.endswith('00'):
# The first level category, using the code bellow.
cat_con = TabPost2Tag.par_id == cat_id
else:
cat_con = TabPost2Tag.tag_id == cat_id
if tag:
condition = {
'def_tag_arr': [tag]
}
recs = TabPost.select().join(
TabPost2Tag,
on=((TabPost.uid == TabPost2Tag.post_id) & (TabPost.valid == 1))
).where(
cat_con & TabPost.extinfo.contains(condition)
).order_by(
TabPost.time_update.desc()
).paginate(current_page_num, CMS_CFG['list_num'])
elif order:
recs = TabPost.select().join(
TabPost2Tag,
on=((TabPost.uid == TabPost2Tag.post_id) & (TabPost.valid == 1))
).where(
cat_con
).order_by(
TabPost.order.asc()
)
else:
recs = TabPost.select().join(
TabPost2Tag,
on=((TabPost.uid == TabPost2Tag.post_id) & (TabPost.valid == 1))
).where(
cat_con
).order_by(
TabPost.time_update.desc()
).paginate(current_page_num, CMS_CFG['list_num'])
return recs | python | def query_pager_by_slug(slug, current_page_num=1, tag='', order=False):
'''
Query pager via category slug.
'''
cat_rec = MCategory.get_by_slug(slug)
if cat_rec:
cat_id = cat_rec.uid
else:
return None
# The flowing code is valid.
if cat_id.endswith('00'):
# The first level category, using the code bellow.
cat_con = TabPost2Tag.par_id == cat_id
else:
cat_con = TabPost2Tag.tag_id == cat_id
if tag:
condition = {
'def_tag_arr': [tag]
}
recs = TabPost.select().join(
TabPost2Tag,
on=((TabPost.uid == TabPost2Tag.post_id) & (TabPost.valid == 1))
).where(
cat_con & TabPost.extinfo.contains(condition)
).order_by(
TabPost.time_update.desc()
).paginate(current_page_num, CMS_CFG['list_num'])
elif order:
recs = TabPost.select().join(
TabPost2Tag,
on=((TabPost.uid == TabPost2Tag.post_id) & (TabPost.valid == 1))
).where(
cat_con
).order_by(
TabPost.order.asc()
)
else:
recs = TabPost.select().join(
TabPost2Tag,
on=((TabPost.uid == TabPost2Tag.post_id) & (TabPost.valid == 1))
).where(
cat_con
).order_by(
TabPost.time_update.desc()
).paginate(current_page_num, CMS_CFG['list_num'])
return recs | Query pager via category slug. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L203-L251 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.query_by_entity_uid | def query_by_entity_uid(idd, kind=''):
'''
Query post2tag by certain post.
'''
if kind == '':
return TabPost2Tag.select(
TabPost2Tag,
TabTag.slug.alias('tag_slug'),
TabTag.name.alias('tag_name')
).join(
TabTag, on=(TabPost2Tag.tag_id == TabTag.uid)
).where(
(TabPost2Tag.post_id == idd) &
(TabTag.kind != 'z')
).order_by(
TabPost2Tag.order
)
return TabPost2Tag.select(
TabPost2Tag,
TabTag.slug.alias('tag_slug'),
TabTag.name.alias('tag_name')
).join(TabTag, on=(TabPost2Tag.tag_id == TabTag.uid)).where(
(TabTag.kind == kind) &
(TabPost2Tag.post_id == idd)
).order_by(
TabPost2Tag.order
) | python | def query_by_entity_uid(idd, kind=''):
'''
Query post2tag by certain post.
'''
if kind == '':
return TabPost2Tag.select(
TabPost2Tag,
TabTag.slug.alias('tag_slug'),
TabTag.name.alias('tag_name')
).join(
TabTag, on=(TabPost2Tag.tag_id == TabTag.uid)
).where(
(TabPost2Tag.post_id == idd) &
(TabTag.kind != 'z')
).order_by(
TabPost2Tag.order
)
return TabPost2Tag.select(
TabPost2Tag,
TabTag.slug.alias('tag_slug'),
TabTag.name.alias('tag_name')
).join(TabTag, on=(TabPost2Tag.tag_id == TabTag.uid)).where(
(TabTag.kind == kind) &
(TabPost2Tag.post_id == idd)
).order_by(
TabPost2Tag.order
) | Query post2tag by certain post. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L254-L281 |
bukun/TorCMS | torcms/model/post2catalog_model.py | MPost2Catalog.get_first_category | def get_first_category(app_uid):
'''
Get the first, as the uniqe category of post.
'''
recs = MPost2Catalog.query_by_entity_uid(app_uid).objects()
if recs.count() > 0:
return recs.get()
return None | python | def get_first_category(app_uid):
'''
Get the first, as the uniqe category of post.
'''
recs = MPost2Catalog.query_by_entity_uid(app_uid).objects()
if recs.count() > 0:
return recs.get()
return None | Get the first, as the uniqe category of post. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/post2catalog_model.py#L291-L299 |
bukun/TorCMS | torcms/modules/info_modules.py | InfoCategory.render | def render(self, *args, **kwargs):
'''
fun(uid_with_str)
fun(uid_with_str, slug = val1, glyph = val2)
'''
uid_with_str = args[0]
slug = kwargs.get('slug', False)
with_title = kwargs.get('with_title', False)
glyph = kwargs.get('glyph', '')
kwd = {
'glyph': glyph
}
curinfo = MCategory.get_by_uid(uid_with_str)
sub_cats = MCategory.query_sub_cat(uid_with_str)
if slug:
tmpl = 'modules/info/catalog_slug.html'
else:
tmpl = 'modules/info/catalog_of.html'
return self.render_string(tmpl,
pcatinfo=curinfo,
sub_cats=sub_cats,
recs=sub_cats,
with_title=with_title,
kwd=kwd) | python | def render(self, *args, **kwargs):
'''
fun(uid_with_str)
fun(uid_with_str, slug = val1, glyph = val2)
'''
uid_with_str = args[0]
slug = kwargs.get('slug', False)
with_title = kwargs.get('with_title', False)
glyph = kwargs.get('glyph', '')
kwd = {
'glyph': glyph
}
curinfo = MCategory.get_by_uid(uid_with_str)
sub_cats = MCategory.query_sub_cat(uid_with_str)
if slug:
tmpl = 'modules/info/catalog_slug.html'
else:
tmpl = 'modules/info/catalog_of.html'
return self.render_string(tmpl,
pcatinfo=curinfo,
sub_cats=sub_cats,
recs=sub_cats,
with_title=with_title,
kwd=kwd) | fun(uid_with_str)
fun(uid_with_str, slug = val1, glyph = val2) | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/modules/info_modules.py#L25-L56 |
bukun/TorCMS | torcms/modules/info_modules.py | InforUserMost.render | def render(self, *args, **kwargs):
'''
fun(user_name, kind)
fun(user_name, kind, num)
fun(user_name, kind, num, with_tag = val1, glyph = val2)
fun(user_name = vala, kind = valb, num = valc, with_tag = val1, glyph = val2)
'''
user_name = kwargs.get('user_name', args[0])
kind = kwargs.get('kind', args[1])
num = kwargs.get('num', args[2] if len(args) > 2 else 6)
with_tag = kwargs.get('with_tag', False)
glyph = kwargs.get('glyph', '')
all_cats = MUsage.query_most(user_name, kind, num).objects()
kwd = {
'with_tag': with_tag,
'router': router_post[kind],
'glyph': glyph
}
return self.render_string('modules/info/list_user_equation.html',
recs=all_cats,
kwd=kwd) | python | def render(self, *args, **kwargs):
'''
fun(user_name, kind)
fun(user_name, kind, num)
fun(user_name, kind, num, with_tag = val1, glyph = val2)
fun(user_name = vala, kind = valb, num = valc, with_tag = val1, glyph = val2)
'''
user_name = kwargs.get('user_name', args[0])
kind = kwargs.get('kind', args[1])
num = kwargs.get('num', args[2] if len(args) > 2 else 6)
with_tag = kwargs.get('with_tag', False)
glyph = kwargs.get('glyph', '')
all_cats = MUsage.query_most(user_name, kind, num).objects()
kwd = {
'with_tag': with_tag,
'router': router_post[kind],
'glyph': glyph
}
return self.render_string('modules/info/list_user_equation.html',
recs=all_cats,
kwd=kwd) | fun(user_name, kind)
fun(user_name, kind, num)
fun(user_name, kind, num, with_tag = val1, glyph = val2)
fun(user_name = vala, kind = valb, num = valc, with_tag = val1, glyph = val2) | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/modules/info_modules.py#L64-L85 |
bukun/TorCMS | torcms/modules/info_modules.py | InfoMostUsed.render_it | def render_it(self, *args, **kwargs):
'''
Render without userinfo.
fun(kind, num)
fun(kind, num, with_tag = val1)
fun(kind, num, with_tag = val1, glyph = val2)
'''
kind = kwargs.get('kind', args[0])
num = kwargs.get('num', args[1] if len(args) > 1 else 6)
with_tag = kwargs.get('with_tag', False)
glyph = kwargs.get('glyph', '')
all_cats = MPost.query_most(kind=kind, num=num).objects()
kwd = {
'with_tag': with_tag,
'router': router_post[kind],
'glyph': glyph
}
return self.render_string('modules/info/list_equation.html',
recs=all_cats,
kwd=kwd) | python | def render_it(self, *args, **kwargs):
'''
Render without userinfo.
fun(kind, num)
fun(kind, num, with_tag = val1)
fun(kind, num, with_tag = val1, glyph = val2)
'''
kind = kwargs.get('kind', args[0])
num = kwargs.get('num', args[1] if len(args) > 1 else 6)
with_tag = kwargs.get('with_tag', False)
glyph = kwargs.get('glyph', '')
all_cats = MPost.query_most(kind=kind, num=num).objects()
kwd = {
'with_tag': with_tag,
'router': router_post[kind],
'glyph': glyph
}
return self.render_string('modules/info/list_equation.html',
recs=all_cats,
kwd=kwd) | Render without userinfo.
fun(kind, num)
fun(kind, num, with_tag = val1)
fun(kind, num, with_tag = val1, glyph = val2) | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/modules/info_modules.py#L112-L132 |
bukun/TorCMS | torcms/modules/info_modules.py | InfoRecentUsed.render_it | def render_it(self, kind, num, with_tag=False, glyph=''):
'''
render, no user logged in
'''
all_cats = MPost.query_recent(num, kind=kind)
kwd = {
'with_tag': with_tag,
'router': router_post[kind],
'glyph': glyph
}
return self.render_string('modules/info/list_equation.html',
recs=all_cats,
kwd=kwd) | python | def render_it(self, kind, num, with_tag=False, glyph=''):
'''
render, no user logged in
'''
all_cats = MPost.query_recent(num, kind=kind)
kwd = {
'with_tag': with_tag,
'router': router_post[kind],
'glyph': glyph
}
return self.render_string('modules/info/list_equation.html',
recs=all_cats,
kwd=kwd) | render, no user logged in | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/modules/info_modules.py#L185-L197 |
bukun/TorCMS | torcms/modules/info_modules.py | InfoRecentUsed.render_user | def render_user(self, *args, **kwargs):
'''
render, with userinfo
fun(kind, num)
fun(kind, num, with_tag = val1)
fun(kind, num, with_tag = val1, user_id = val2)
fun(kind, num, with_tag = val1, user_id = val2, glyph = val3)
'''
kind = kwargs.get('kind', args[0])
num = kwargs.get('num', args[1] if len(args) > 1 else 6)
with_tag = kwargs.get('with_tag', False)
user_id = kwargs.get('user_id', '')
glyph = kwargs.get('glyph', '')
logger.info(
'Infor user recent, username: {user_name}, kind: {kind}, num: {num}'.format(
user_name=user_id, kind=kind, num=num
)
)
all_cats = MUsage.query_recent(user_id, kind, num).objects()
kwd = {
'with_tag': with_tag,
'router': router_post[kind],
'glyph': glyph
}
return self.render_string('modules/info/list_user_equation.html',
recs=all_cats,
kwd=kwd) | python | def render_user(self, *args, **kwargs):
'''
render, with userinfo
fun(kind, num)
fun(kind, num, with_tag = val1)
fun(kind, num, with_tag = val1, user_id = val2)
fun(kind, num, with_tag = val1, user_id = val2, glyph = val3)
'''
kind = kwargs.get('kind', args[0])
num = kwargs.get('num', args[1] if len(args) > 1 else 6)
with_tag = kwargs.get('with_tag', False)
user_id = kwargs.get('user_id', '')
glyph = kwargs.get('glyph', '')
logger.info(
'Infor user recent, username: {user_name}, kind: {kind}, num: {num}'.format(
user_name=user_id, kind=kind, num=num
)
)
all_cats = MUsage.query_recent(user_id, kind, num).objects()
kwd = {
'with_tag': with_tag,
'router': router_post[kind],
'glyph': glyph
}
return self.render_string('modules/info/list_user_equation.html',
recs=all_cats,
kwd=kwd) | render, with userinfo
fun(kind, num)
fun(kind, num, with_tag = val1)
fun(kind, num, with_tag = val1, user_id = val2)
fun(kind, num, with_tag = val1, user_id = val2, glyph = val3) | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/modules/info_modules.py#L199-L228 |
bukun/TorCMS | torcms/handlers/link_handler.py | LinkHandler.recent | def recent(self):
'''
Recent links.
'''
kwd = {
'pager': '',
'title': '最近文档',
}
if self.is_p:
self.render('admin/link_ajax/link_list.html',
kwd=kwd,
view=MLink.query_link(20),
format_date=tools.format_date,
userinfo=self.userinfo)
else:
self.render('misc/link/link_list.html',
kwd=kwd,
view=MLink.query_link(20),
format_date=tools.format_date,
userinfo=self.userinfo) | python | def recent(self):
'''
Recent links.
'''
kwd = {
'pager': '',
'title': '最近文档',
}
if self.is_p:
self.render('admin/link_ajax/link_list.html',
kwd=kwd,
view=MLink.query_link(20),
format_date=tools.format_date,
userinfo=self.userinfo)
else:
self.render('misc/link/link_list.html',
kwd=kwd,
view=MLink.query_link(20),
format_date=tools.format_date,
userinfo=self.userinfo) | Recent links. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L58-L78 |
bukun/TorCMS | torcms/handlers/link_handler.py | LinkHandler.to_add_link | def to_add_link(self, ):
'''
To add link
'''
if self.check_post_role()['ADD']:
pass
else:
return False
kwd = {
'pager': '',
'uid': '',
}
self.render('misc/link/link_add.html',
topmenu='',
kwd=kwd,
userinfo=self.userinfo, ) | python | def to_add_link(self, ):
'''
To add link
'''
if self.check_post_role()['ADD']:
pass
else:
return False
kwd = {
'pager': '',
'uid': '',
}
self.render('misc/link/link_add.html',
topmenu='',
kwd=kwd,
userinfo=self.userinfo, ) | To add link | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L80-L95 |
bukun/TorCMS | torcms/handlers/link_handler.py | LinkHandler.update | def update(self, uid):
'''
Update the link.
'''
if self.userinfo.role[1] >= '3':
pass
else:
return False
post_data = self.get_post_data()
post_data['user_name'] = self.get_current_user()
if self.is_p:
if MLink.update(uid, post_data):
output = {
'addinfo ': 1,
}
else:
output = {
'addinfo ': 0,
}
return json.dump(output, self)
else:
if MLink.update(uid, post_data):
self.redirect('/link/list') | python | def update(self, uid):
'''
Update the link.
'''
if self.userinfo.role[1] >= '3':
pass
else:
return False
post_data = self.get_post_data()
post_data['user_name'] = self.get_current_user()
if self.is_p:
if MLink.update(uid, post_data):
output = {
'addinfo ': 1,
}
else:
output = {
'addinfo ': 0,
}
return json.dump(output, self)
else:
if MLink.update(uid, post_data):
self.redirect('/link/list') | Update the link. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L98-L122 |
bukun/TorCMS | torcms/handlers/link_handler.py | LinkHandler.to_modify | def to_modify(self, uid):
'''
Try to edit the link.
'''
if self.userinfo.role[1] >= '3':
pass
else:
return False
self.render('misc/link/link_edit.html',
kwd={},
postinfo=MLink.get_by_uid(uid),
userinfo=self.userinfo) | python | def to_modify(self, uid):
'''
Try to edit the link.
'''
if self.userinfo.role[1] >= '3':
pass
else:
return False
self.render('misc/link/link_edit.html',
kwd={},
postinfo=MLink.get_by_uid(uid),
userinfo=self.userinfo) | Try to edit the link. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L125-L137 |
bukun/TorCMS | torcms/handlers/link_handler.py | LinkHandler.viewit | def viewit(self, post_id):
'''
View the link.
'''
rec = MLink.get_by_uid(post_id)
if not rec:
kwd = {'info': '您要找的分类不存在。'}
self.render('misc/html/404.html', kwd=kwd)
return False
kwd = {
'pager': '',
'editable': self.editable(),
}
self.render('misc/link/link_view.html',
view=rec,
kwd=kwd,
userinfo=self.userinfo,
cfg=CMS_CFG, ) | python | def viewit(self, post_id):
'''
View the link.
'''
rec = MLink.get_by_uid(post_id)
if not rec:
kwd = {'info': '您要找的分类不存在。'}
self.render('misc/html/404.html', kwd=kwd)
return False
kwd = {
'pager': '',
'editable': self.editable(),
}
self.render('misc/link/link_view.html',
view=rec,
kwd=kwd,
userinfo=self.userinfo,
cfg=CMS_CFG, ) | View the link. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L140-L161 |
bukun/TorCMS | torcms/handlers/link_handler.py | LinkHandler.p_user_add_link | def p_user_add_link(self):
'''
user add link.
'''
if self.check_post_role()['ADD']:
pass
else:
return False
post_data = self.get_post_data()
post_data['user_name'] = self.get_current_user()
cur_uid = tools.get_uudd(2)
while MLink.get_by_uid(cur_uid):
cur_uid = tools.get_uudd(2)
if MLink.create_link(cur_uid, post_data):
output = {
'addinfo ': 1,
}
else:
output = {
'addinfo ': 0,
}
return json.dump(output, self) | python | def p_user_add_link(self):
'''
user add link.
'''
if self.check_post_role()['ADD']:
pass
else:
return False
post_data = self.get_post_data()
post_data['user_name'] = self.get_current_user()
cur_uid = tools.get_uudd(2)
while MLink.get_by_uid(cur_uid):
cur_uid = tools.get_uudd(2)
if MLink.create_link(cur_uid, post_data):
output = {
'addinfo ': 1,
}
else:
output = {
'addinfo ': 0,
}
return json.dump(output, self) | user add link. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L164-L188 |
bukun/TorCMS | torcms/handlers/link_handler.py | LinkHandler.user_add_link | def user_add_link(self):
'''
Create link by user.
'''
if self.check_post_role()['ADD']:
pass
else:
return False
post_data = self.get_post_data()
post_data['user_name'] = self.get_current_user()
cur_uid = tools.get_uudd(2)
while MLink.get_by_uid(cur_uid):
cur_uid = tools.get_uudd(2)
MLink.create_link(cur_uid, post_data)
self.redirect('/link/list') | python | def user_add_link(self):
'''
Create link by user.
'''
if self.check_post_role()['ADD']:
pass
else:
return False
post_data = self.get_post_data()
post_data['user_name'] = self.get_current_user()
cur_uid = tools.get_uudd(2)
while MLink.get_by_uid(cur_uid):
cur_uid = tools.get_uudd(2)
MLink.create_link(cur_uid, post_data)
self.redirect('/link/list') | Create link by user. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L191-L209 |
bukun/TorCMS | torcms/handlers/link_handler.py | LinkHandler.delete_by_id | def delete_by_id(self, del_id):
'''
Delete a link by id.
'''
if self.check_post_role()['DELETE']:
pass
else:
return False
if self.is_p:
if MLink.delete(del_id):
output = {'del_link': 1}
else:
output = {'del_link': 0}
return json.dump(output, self)
else:
is_deleted = MLink.delete(del_id)
if is_deleted:
self.redirect('/link/list') | python | def delete_by_id(self, del_id):
'''
Delete a link by id.
'''
if self.check_post_role()['DELETE']:
pass
else:
return False
if self.is_p:
if MLink.delete(del_id):
output = {'del_link': 1}
else:
output = {'del_link': 0}
return json.dump(output, self)
else:
is_deleted = MLink.delete(del_id)
if is_deleted:
self.redirect('/link/list') | Delete a link by id. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/link_handler.py#L212-L229 |
bukun/TorCMS | torcms/model/rating_model.py | MRating.get_rating | def get_rating(postid, userid):
'''
Get the rating of certain post and user.
'''
try:
recs = TabRating.select().where(
(TabRating.post_id == postid) & (TabRating.user_id == userid)
)
except:
return False
if recs.count() > 0:
return recs.get().rating
else:
return False | python | def get_rating(postid, userid):
'''
Get the rating of certain post and user.
'''
try:
recs = TabRating.select().where(
(TabRating.post_id == postid) & (TabRating.user_id == userid)
)
except:
return False
if recs.count() > 0:
return recs.get().rating
else:
return False | Get the rating of certain post and user. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/rating_model.py#L31-L44 |
bukun/TorCMS | torcms/model/rating_model.py | MRating.update | def update(postid, userid, rating):
'''
Update the rating of certain post and user.
The record will be created if no record exists.
'''
rating_recs = TabRating.select().where(
(TabRating.post_id == postid) & (TabRating.user_id == userid)
)
if rating_recs.count() > 0:
MRating.__update_rating(rating_recs.get().uid, rating)
else:
MRating.__insert_data(postid, userid, rating) | python | def update(postid, userid, rating):
'''
Update the rating of certain post and user.
The record will be created if no record exists.
'''
rating_recs = TabRating.select().where(
(TabRating.post_id == postid) & (TabRating.user_id == userid)
)
if rating_recs.count() > 0:
MRating.__update_rating(rating_recs.get().uid, rating)
else:
MRating.__insert_data(postid, userid, rating) | Update the rating of certain post and user.
The record will be created if no record exists. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/rating_model.py#L47-L58 |
bukun/TorCMS | torcms/model/rating_model.py | MRating.__update_rating | def __update_rating(uid, rating):
'''
Update rating.
'''
entry = TabRating.update(
rating=rating
).where(TabRating.uid == uid)
entry.execute() | python | def __update_rating(uid, rating):
'''
Update rating.
'''
entry = TabRating.update(
rating=rating
).where(TabRating.uid == uid)
entry.execute() | Update rating. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/rating_model.py#L61-L68 |
bukun/TorCMS | torcms/model/rating_model.py | MRating.__insert_data | def __insert_data(postid, userid, rating):
'''
Inert new record.
'''
uid = tools.get_uuid()
TabRating.create(
uid=uid,
post_id=postid,
user_id=userid,
rating=rating,
timestamp=tools.timestamp(),
)
return uid | python | def __insert_data(postid, userid, rating):
'''
Inert new record.
'''
uid = tools.get_uuid()
TabRating.create(
uid=uid,
post_id=postid,
user_id=userid,
rating=rating,
timestamp=tools.timestamp(),
)
return uid | Inert new record. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/rating_model.py#L71-L83 |
bukun/TorCMS | helper_scripts/script_meta_xlsx_import.py | update_category | def update_category(uid, postdata, kwargs):
'''
Update the category of the post.
'''
catid = kwargs['catid'] if ('catid' in kwargs and MCategory.get_by_uid(kwargs['catid'])) else None
post_data = postdata
current_infos = MPost2Catalog.query_by_entity_uid(uid, kind='').objects()
new_category_arr = []
# Used to update post2category, to keep order.
def_cate_arr = ['gcat{0}'.format(x) for x in range(10)]
# for old page.
def_cate_arr.append('def_cat_uid')
# Used to update post extinfo.
cat_dic = {}
for key in def_cate_arr:
if key not in post_data:
continue
if post_data[key] == '' or post_data[key] == '0':
continue
# 有可能选重复了。保留前面的
if post_data[key] in new_category_arr:
continue
new_category_arr.append(post_data[key] + ' ' * (4 - len(post_data[key])))
cat_dic[key] = post_data[key] + ' ' * (4 - len(post_data[key]))
if catid:
def_cat_id = catid
elif new_category_arr:
def_cat_id = new_category_arr[0]
else:
def_cat_id = None
if def_cat_id:
cat_dic['def_cat_uid'] = def_cat_id
cat_dic['def_cat_pid'] = MCategory.get_by_uid(def_cat_id).pid
print('=' * 40)
print(uid)
print(cat_dic)
MPost.update_jsonb(uid, cat_dic)
for index, catid in enumerate(new_category_arr):
MPost2Catalog.add_record(uid, catid, index)
# Delete the old category if not in post requests.
for cur_info in current_infos:
if cur_info.tag_id not in new_category_arr:
MPost2Catalog.remove_relation(uid, cur_info.tag_id) | python | def update_category(uid, postdata, kwargs):
'''
Update the category of the post.
'''
catid = kwargs['catid'] if ('catid' in kwargs and MCategory.get_by_uid(kwargs['catid'])) else None
post_data = postdata
current_infos = MPost2Catalog.query_by_entity_uid(uid, kind='').objects()
new_category_arr = []
# Used to update post2category, to keep order.
def_cate_arr = ['gcat{0}'.format(x) for x in range(10)]
# for old page.
def_cate_arr.append('def_cat_uid')
# Used to update post extinfo.
cat_dic = {}
for key in def_cate_arr:
if key not in post_data:
continue
if post_data[key] == '' or post_data[key] == '0':
continue
# 有可能选重复了。保留前面的
if post_data[key] in new_category_arr:
continue
new_category_arr.append(post_data[key] + ' ' * (4 - len(post_data[key])))
cat_dic[key] = post_data[key] + ' ' * (4 - len(post_data[key]))
if catid:
def_cat_id = catid
elif new_category_arr:
def_cat_id = new_category_arr[0]
else:
def_cat_id = None
if def_cat_id:
cat_dic['def_cat_uid'] = def_cat_id
cat_dic['def_cat_pid'] = MCategory.get_by_uid(def_cat_id).pid
print('=' * 40)
print(uid)
print(cat_dic)
MPost.update_jsonb(uid, cat_dic)
for index, catid in enumerate(new_category_arr):
MPost2Catalog.add_record(uid, catid, index)
# Delete the old category if not in post requests.
for cur_info in current_infos:
if cur_info.tag_id not in new_category_arr:
MPost2Catalog.remove_relation(uid, cur_info.tag_id) | Update the category of the post. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/helper_scripts/script_meta_xlsx_import.py#L19-L72 |
bukun/TorCMS | ext_script/command.py | entry | def entry(argv):
'''
Command entry
'''
command_dic = {
'init': run_init,
}
try:
# 这里的 h 就表示该选项无参数，i:表示 i 选项后需要有参数
opts, args = getopt.getopt(argv, "hi:")
except getopt.GetoptError:
print('Error: helper.py -i cmd')
sys.exit(2)
for opt, arg in opts:
if opt == "-h":
print('helper.py -i cmd')
print('cmd list ----------------------')
print(' init: ')
sys.exit()
elif opt == "-i":
if arg in command_dic:
command_dic[arg](args)
print('QED!')
else:
print('Wrong Command.') | python | def entry(argv):
'''
Command entry
'''
command_dic = {
'init': run_init,
}
try:
# 这里的 h 就表示该选项无参数，i:表示 i 选项后需要有参数
opts, args = getopt.getopt(argv, "hi:")
except getopt.GetoptError:
print('Error: helper.py -i cmd')
sys.exit(2)
for opt, arg in opts:
if opt == "-h":
print('helper.py -i cmd')
print('cmd list ----------------------')
print(' init: ')
sys.exit()
elif opt == "-i":
if arg in command_dic:
command_dic[arg](args)
print('QED!')
else:
print('Wrong Command.') | Command entry | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/ext_script/command.py#L16-L47 |
bukun/TorCMS | torcms/model/relation_model.py | MRelation.add_relation | def add_relation(app_f, app_t, weight=1):
'''
Adding relation between two posts.
'''
recs = TabRel.select().where(
(TabRel.post_f_id == app_f) & (TabRel.post_t_id == app_t)
)
if recs.count() > 1:
for record in recs:
MRelation.delete(record.uid)
if recs.count() == 0:
uid = tools.get_uuid()
entry = TabRel.create(
uid=uid,
post_f_id=app_f,
post_t_id=app_t,
count=1,
)
return entry.uid
elif recs.count() == 1:
MRelation.update_relation(app_f, app_t, weight)
else:
return False | python | def add_relation(app_f, app_t, weight=1):
'''
Adding relation between two posts.
'''
recs = TabRel.select().where(
(TabRel.post_f_id == app_f) & (TabRel.post_t_id == app_t)
)
if recs.count() > 1:
for record in recs:
MRelation.delete(record.uid)
if recs.count() == 0:
uid = tools.get_uuid()
entry = TabRel.create(
uid=uid,
post_f_id=app_f,
post_t_id=app_t,
count=1,
)
return entry.uid
elif recs.count() == 1:
MRelation.update_relation(app_f, app_t, weight)
else:
return False | Adding relation between two posts. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/relation_model.py#L15-L38 |
bukun/TorCMS | torcms/model/relation_model.py | MRelation.get_app_relations | def get_app_relations(app_id, num=20, kind='1'):
'''
The the related infors.
'''
info_tag = MInfor2Catalog.get_first_category(app_id)
if info_tag:
return TabPost2Tag.select(
TabPost2Tag,
TabPost.title.alias('post_title'),
TabPost.valid.alias('post_valid')
).join(
TabPost, on=(TabPost2Tag.post_id == TabPost.uid)
).where(
(TabPost2Tag.tag_id == info_tag.tag_id) &
(TabPost.kind == kind)
).order_by(
peewee.fn.Random()
).limit(num)
return TabPost2Tag.select(
TabPost2Tag,
TabPost.title.alias('post_title'),
TabPost.valid.alias('post_valid')
).join(
TabPost, on=(TabPost2Tag.post_id == TabPost.uid)
).where(
TabPost.kind == kind
).order_by(peewee.fn.Random()).limit(num) | python | def get_app_relations(app_id, num=20, kind='1'):
'''
The the related infors.
'''
info_tag = MInfor2Catalog.get_first_category(app_id)
if info_tag:
return TabPost2Tag.select(
TabPost2Tag,
TabPost.title.alias('post_title'),
TabPost.valid.alias('post_valid')
).join(
TabPost, on=(TabPost2Tag.post_id == TabPost.uid)
).where(
(TabPost2Tag.tag_id == info_tag.tag_id) &
(TabPost.kind == kind)
).order_by(
peewee.fn.Random()
).limit(num)
return TabPost2Tag.select(
TabPost2Tag,
TabPost.title.alias('post_title'),
TabPost.valid.alias('post_valid')
).join(
TabPost, on=(TabPost2Tag.post_id == TabPost.uid)
).where(
TabPost.kind == kind
).order_by(peewee.fn.Random()).limit(num) | The the related infors. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/relation_model.py#L63-L89 |
bukun/TorCMS | torcms/handlers/label_handler.py | LabelHandler.get | def get(self, *args, **kwargs):
'''
/label/s/view
'''
url_arr = self.parse_url(args[0])
if len(url_arr) == 2:
if url_arr[0] == 'remove':
self.remove_redis_keyword(url_arr[1])
else:
self.list(url_arr[0], url_arr[1])
elif len(url_arr) == 3:
self.list(url_arr[0], url_arr[1], url_arr[2])
else:
return False | python | def get(self, *args, **kwargs):
'''
/label/s/view
'''
url_arr = self.parse_url(args[0])
if len(url_arr) == 2:
if url_arr[0] == 'remove':
self.remove_redis_keyword(url_arr[1])
else:
self.list(url_arr[0], url_arr[1])
elif len(url_arr) == 3:
self.list(url_arr[0], url_arr[1], url_arr[2])
else:
return False | /label/s/view | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/label_handler.py#L27-L41 |
bukun/TorCMS | torcms/handlers/label_handler.py | LabelHandler.remove_redis_keyword | def remove_redis_keyword(self, keyword):
'''
Remove the keyword for redis.
'''
redisvr.srem(CMS_CFG['redis_kw'] + self.userinfo.user_name, keyword)
return json.dump({}, self) | python | def remove_redis_keyword(self, keyword):
'''
Remove the keyword for redis.
'''
redisvr.srem(CMS_CFG['redis_kw'] + self.userinfo.user_name, keyword)
return json.dump({}, self) | Remove the keyword for redis. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/label_handler.py#L44-L49 |
bukun/TorCMS | torcms/handlers/label_handler.py | LabelHandler.list | def list(self, kind, tag_slug, cur_p=''):
'''
根据 cat_handler.py 中的 def view_cat_new(self, cat_slug, cur_p = '')
'''
# 下面用来使用关键字过滤信息，如果网站信息量不是很大不要开启
# Todo:
# if self.get_current_user():
# redisvr.sadd(config.redis_kw + self.userinfo.user_name, tag_slug)
if cur_p == '':
current_page_number = 1
else:
current_page_number = int(cur_p)
current_page_number = 1 if current_page_number < 1 else current_page_number
pager_num = int(MPost2Label.total_number(tag_slug, kind) / CMS_CFG['list_num'])
tag_info = MLabel.get_by_slug(tag_slug)
if tag_info:
tag_name = tag_info.name
else:
tag_name = 'Label search results'
kwd = {'tag_name': tag_name,
'tag_slug': tag_slug,
'title': tag_name,
'current_page': current_page_number,
'router': router_post[kind],
'kind': kind
}
the_list_file = './templates/list/label_{kind}.html'.format(kind=kind)
if os.path.exists(the_list_file):
tmpl = 'list/label_{kind}.html'.format(kind=kind)
else:
tmpl = 'list/label.html'
self.render(tmpl,
infos=MPost2Label.query_pager_by_slug(
tag_slug,
kind=kind,
current_page_num=current_page_number
),
kwd=kwd,
userinfo=self.userinfo,
pager=self.gen_pager(kind, tag_slug, pager_num, current_page_number),
cfg=CMS_CFG) | python | def list(self, kind, tag_slug, cur_p=''):
'''
根据 cat_handler.py 中的 def view_cat_new(self, cat_slug, cur_p = '')
'''
# 下面用来使用关键字过滤信息，如果网站信息量不是很大不要开启
# Todo:
# if self.get_current_user():
# redisvr.sadd(config.redis_kw + self.userinfo.user_name, tag_slug)
if cur_p == '':
current_page_number = 1
else:
current_page_number = int(cur_p)
current_page_number = 1 if current_page_number < 1 else current_page_number
pager_num = int(MPost2Label.total_number(tag_slug, kind) / CMS_CFG['list_num'])
tag_info = MLabel.get_by_slug(tag_slug)
if tag_info:
tag_name = tag_info.name
else:
tag_name = 'Label search results'
kwd = {'tag_name': tag_name,
'tag_slug': tag_slug,
'title': tag_name,
'current_page': current_page_number,
'router': router_post[kind],
'kind': kind
}
the_list_file = './templates/list/label_{kind}.html'.format(kind=kind)
if os.path.exists(the_list_file):
tmpl = 'list/label_{kind}.html'.format(kind=kind)
else:
tmpl = 'list/label.html'
self.render(tmpl,
infos=MPost2Label.query_pager_by_slug(
tag_slug,
kind=kind,
current_page_num=current_page_number
),
kwd=kwd,
userinfo=self.userinfo,
pager=self.gen_pager(kind, tag_slug, pager_num, current_page_number),
cfg=CMS_CFG) | 根据 cat_handler.py 中的 def view_cat_new(self, cat_slug, cur_p = '') | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/label_handler.py#L51-L98 |
bukun/TorCMS | torcms/handlers/label_handler.py | LabelHandler.gen_pager | def gen_pager(self, kind, cat_slug, page_num, current):
'''
cat_slug 分类
page_num 页面总数
current 当前页面
'''
if page_num == 1:
return ''
pager_shouye = '''<li class="{0}"> <a href="/label/{1}/{2}"><< 首页</a>
</li>'''.format(
'hidden' if current <= 1 else '', kind, cat_slug
)
pager_pre = '''<li class="{0}"><a href="/label/{1}/{2}/{3}">< 前页</a>
</li>'''.format(
'hidden' if current <= 1 else '', kind, cat_slug, current - 1
)
pager_mid = ''
for ind in range(0, page_num):
tmp_mid = '''<li class="{0}"><a href="/label/{1}/{2}/{3}">{3}</a>
</li>'''.format(
'active' if ind + 1 == current else '', kind, cat_slug, ind + 1
)
pager_mid += tmp_mid
pager_next = '''<li class=" {0}"><a href="/label/{1}/{2}/{3}">后页 ></a>
</li>'''.format(
'hidden' if current >= page_num else '', kind, cat_slug, current + 1
)
pager_last = '''<li class=" {0}"><a href="/label/{1}/{2}/{3}">末页>></a>
</li>'''.format(
'hidden' if current >= page_num else '', kind, cat_slug, page_num
)
pager = pager_shouye + pager_pre + pager_mid + pager_next + pager_last
return pager | python | def gen_pager(self, kind, cat_slug, page_num, current):
'''
cat_slug 分类
page_num 页面总数
current 当前页面
'''
if page_num == 1:
return ''
pager_shouye = '''<li class="{0}"> <a href="/label/{1}/{2}"><< 首页</a>
</li>'''.format(
'hidden' if current <= 1 else '', kind, cat_slug
)
pager_pre = '''<li class="{0}"><a href="/label/{1}/{2}/{3}">< 前页</a>
</li>'''.format(
'hidden' if current <= 1 else '', kind, cat_slug, current - 1
)
pager_mid = ''
for ind in range(0, page_num):
tmp_mid = '''<li class="{0}"><a href="/label/{1}/{2}/{3}">{3}</a>
</li>'''.format(
'active' if ind + 1 == current else '', kind, cat_slug, ind + 1
)
pager_mid += tmp_mid
pager_next = '''<li class=" {0}"><a href="/label/{1}/{2}/{3}">后页 ></a>
</li>'''.format(
'hidden' if current >= page_num else '', kind, cat_slug, current + 1
)
pager_last = '''<li class=" {0}"><a href="/label/{1}/{2}/{3}">末页>></a>
</li>'''.format(
'hidden' if current >= page_num else '', kind, cat_slug, page_num
)
pager = pager_shouye + pager_pre + pager_mid + pager_next + pager_last
return pager | cat_slug 分类
page_num 页面总数
current 当前页面 | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/label_handler.py#L100-L134 |
bukun/TorCMS | torcms/script/script_funcs.py | build_directory | def build_directory():
'''
Build the directory for Whoosh database, and locale.
'''
if os.path.exists('locale'):
pass
else:
os.mkdir('locale')
if os.path.exists(WHOOSH_DB_DIR):
pass
else:
os.makedirs(WHOOSH_DB_DIR) | python | def build_directory():
'''
Build the directory for Whoosh database, and locale.
'''
if os.path.exists('locale'):
pass
else:
os.mkdir('locale')
if os.path.exists(WHOOSH_DB_DIR):
pass
else:
os.makedirs(WHOOSH_DB_DIR) | Build the directory for Whoosh database, and locale. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_funcs.py#L22-L33 |
bukun/TorCMS | torcms/script/script_funcs.py | run_check_kind | def run_check_kind(_):
'''
Running the script.
'''
for kindv in router_post:
for rec_cat in MCategory.query_all(kind=kindv):
catid = rec_cat.uid
catinfo = MCategory.get_by_uid(catid)
for rec_post2tag in MPost2Catalog.query_by_catid(catid):
postinfo = MPost.get_by_uid(rec_post2tag.post_id)
if postinfo.kind == catinfo.kind:
pass
else:
print(postinfo.uid) | python | def run_check_kind(_):
'''
Running the script.
'''
for kindv in router_post:
for rec_cat in MCategory.query_all(kind=kindv):
catid = rec_cat.uid
catinfo = MCategory.get_by_uid(catid)
for rec_post2tag in MPost2Catalog.query_by_catid(catid):
postinfo = MPost.get_by_uid(rec_post2tag.post_id)
if postinfo.kind == catinfo.kind:
pass
else:
print(postinfo.uid) | Running the script. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_funcs.py#L36-L49 |
bukun/TorCMS | torcms/script/script_funcs.py | run_create_admin | def run_create_admin(*args):
'''
creating the default administrator.
'''
post_data = {
'user_name': 'giser',
'user_email': '[email protected]',
'user_pass': '131322',
'role': '3300',
}
if MUser.get_by_name(post_data['user_name']):
print('User {user_name} already exists.'.format(user_name='giser'))
else:
MUser.create_user(post_data) | python | def run_create_admin(*args):
'''
creating the default administrator.
'''
post_data = {
'user_name': 'giser',
'user_email': '[email protected]',
'user_pass': '131322',
'role': '3300',
}
if MUser.get_by_name(post_data['user_name']):
print('User {user_name} already exists.'.format(user_name='giser'))
else:
MUser.create_user(post_data) | creating the default administrator. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_funcs.py#L52-L65 |
bukun/TorCMS | torcms/script/script_funcs.py | run_update_cat | def run_update_cat(_):
'''
Update the catagery.
'''
recs = MPost2Catalog.query_all().objects()
for rec in recs:
if rec.tag_kind != 'z':
print('-' * 40)
print(rec.uid)
print(rec.tag_id)
print(rec.par_id)
MPost2Catalog.update_field(rec.uid, par_id=rec.tag_id[:2] + "00") | python | def run_update_cat(_):
'''
Update the catagery.
'''
recs = MPost2Catalog.query_all().objects()
for rec in recs:
if rec.tag_kind != 'z':
print('-' * 40)
print(rec.uid)
print(rec.tag_id)
print(rec.par_id)
MPost2Catalog.update_field(rec.uid, par_id=rec.tag_id[:2] + "00") | Update the catagery. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_funcs.py#L75-L87 |
bukun/TorCMS | torcms/handlers/rating_handler.py | RatingHandler.update_post | def update_post(self, postid):
'''
The rating of Post should be updaed if the count is greater than 10
'''
voted_recs = MRating.query_by_post(postid)
if voted_recs.count() > 10:
rating = MRating.query_average_rating(postid)
else:
rating = 5
logger.info('Get post rating: {rating}'.format(rating=rating))
# MPost.__update_rating(postid, rating)
MPost.update_misc(postid, rating=rating) | python | def update_post(self, postid):
'''
The rating of Post should be updaed if the count is greater than 10
'''
voted_recs = MRating.query_by_post(postid)
if voted_recs.count() > 10:
rating = MRating.query_average_rating(postid)
else:
rating = 5
logger.info('Get post rating: {rating}'.format(rating=rating))
# MPost.__update_rating(postid, rating)
MPost.update_misc(postid, rating=rating) | The rating of Post should be updaed if the count is greater than 10 | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/rating_handler.py#L35-L49 |
bukun/TorCMS | torcms/handlers/rating_handler.py | RatingHandler.update_rating | def update_rating(self, postid):
'''
only the used who logged in would voting.
'''
post_data = self.get_post_data()
rating = float(post_data['rating'])
postinfo = MPost.get_by_uid(postid)
if postinfo and self.userinfo:
MRating.update(postinfo.uid, self.userinfo.uid, rating=rating)
self.update_post(postid)
else:
return False | python | def update_rating(self, postid):
'''
only the used who logged in would voting.
'''
post_data = self.get_post_data()
rating = float(post_data['rating'])
postinfo = MPost.get_by_uid(postid)
if postinfo and self.userinfo:
MRating.update(postinfo.uid, self.userinfo.uid, rating=rating)
self.update_post(postid)
else:
return False | only the used who logged in would voting. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/rating_handler.py#L52-L63 |
bukun/TorCMS | torcms/script/script_review.py | __get_diff_recent | def __get_diff_recent():
'''
Generate the difference of posts. recently.
'''
diff_str = ''
for key in router_post:
recent_posts = MPost.query_recent_edited(tools.timestamp() - TIME_LIMIT, kind=key)
for recent_post in recent_posts:
hist_rec = MPostHist.get_last(recent_post.uid)
if hist_rec:
raw_title = hist_rec.title
new_title = recent_post.title
infobox = diff_table(raw_title, new_title)
diff_str = diff_str + '''
<h2 style="color:red;font-size:larger;font-weight:70;">TITLE: {0}</h2>
'''.format(recent_post.title) + infobox
infobox = diff_table(hist_rec.cnt_md, recent_post.cnt_md)
diff_str = diff_str + '<h3>CONTENT:{0}</h3>'.format(
recent_post.title
) + infobox + '</hr>'
else:
continue
return diff_str | python | def __get_diff_recent():
'''
Generate the difference of posts. recently.
'''
diff_str = ''
for key in router_post:
recent_posts = MPost.query_recent_edited(tools.timestamp() - TIME_LIMIT, kind=key)
for recent_post in recent_posts:
hist_rec = MPostHist.get_last(recent_post.uid)
if hist_rec:
raw_title = hist_rec.title
new_title = recent_post.title
infobox = diff_table(raw_title, new_title)
diff_str = diff_str + '''
<h2 style="color:red;font-size:larger;font-weight:70;">TITLE: {0}</h2>
'''.format(recent_post.title) + infobox
infobox = diff_table(hist_rec.cnt_md, recent_post.cnt_md)
diff_str = diff_str + '<h3>CONTENT:{0}</h3>'.format(
recent_post.title
) + infobox + '</hr>'
else:
continue
return diff_str | Generate the difference of posts. recently. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_review.py#L23-L50 |
bukun/TorCMS | torcms/script/script_review.py | __get_wiki_review | def __get_wiki_review(email_cnt, idx):
'''
Review for wikis.
'''
recent_posts = MWiki.query_recent_edited(tools.timestamp() - TIME_LIMIT, kind='2')
for recent_post in recent_posts:
hist_rec = MWikiHist.get_last(recent_post.uid)
if hist_rec:
foo_str = '''
<tr><td>{0}</td><td>{1}</td><td class="diff_chg">Edit</td><td>{2}</td>
<td><a href="{3}">{3}</a></td></tr>
'''.format(idx, recent_post.user_name, recent_post.title,
os.path.join(SITE_CFG['site_url'], 'page', recent_post.uid))
email_cnt = email_cnt + foo_str
else:
foo_str = '''
<tr><td>{0}</td><td>{1}</td><td class="diff_add">New </td><td>{2}</td>
<td><a href="{3}">{3}</a></td></tr>
'''.format(idx, recent_post.user_name, recent_post.title,
os.path.join(SITE_CFG['site_url'], 'page', recent_post.uid))
email_cnt = email_cnt + foo_str
idx = idx + 1
email_cnt = email_cnt + '</table>'
return email_cnt, idx | python | def __get_wiki_review(email_cnt, idx):
'''
Review for wikis.
'''
recent_posts = MWiki.query_recent_edited(tools.timestamp() - TIME_LIMIT, kind='2')
for recent_post in recent_posts:
hist_rec = MWikiHist.get_last(recent_post.uid)
if hist_rec:
foo_str = '''
<tr><td>{0}</td><td>{1}</td><td class="diff_chg">Edit</td><td>{2}</td>
<td><a href="{3}">{3}</a></td></tr>
'''.format(idx, recent_post.user_name, recent_post.title,
os.path.join(SITE_CFG['site_url'], 'page', recent_post.uid))
email_cnt = email_cnt + foo_str
else:
foo_str = '''
<tr><td>{0}</td><td>{1}</td><td class="diff_add">New </td><td>{2}</td>
<td><a href="{3}">{3}</a></td></tr>
'''.format(idx, recent_post.user_name, recent_post.title,
os.path.join(SITE_CFG['site_url'], 'page', recent_post.uid))
email_cnt = email_cnt + foo_str
idx = idx + 1
email_cnt = email_cnt + '</table>'
return email_cnt, idx | Review for wikis. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_review.py#L53-L76 |
bukun/TorCMS | torcms/script/script_review.py | __get_post_review | def __get_post_review(email_cnt, idx):
'''
Review for posts.
'''
for key in router_post:
recent_posts = MPost.query_recent_edited(tools.timestamp() - TIME_LIMIT, kind=key)
for recent_post in recent_posts:
hist_rec = MPostHist.get_last(recent_post.uid)
if hist_rec:
foo_str = '''
<tr><td>{0}</td><td>{1}</td><td class="diff_chg">Edit</td><td>{2}</td>
<td><a href="{3}">{3}</a></td></tr>
'''.format(idx, recent_post.user_name, recent_post.title,
os.path.join(SITE_CFG['site_url'], router_post[key],
recent_post.uid))
email_cnt = email_cnt + foo_str
else:
foo_str = '''
<tr><td>{0}</td><td>{1}</td><td class="diff_add">New </td><td>{2}</td>
<td><a href="{3}">{3}</a></td></tr>
'''.format(idx, recent_post.user_name, recent_post.title,
os.path.join(SITE_CFG['site_url'], router_post[key],
recent_post.uid))
email_cnt = email_cnt + foo_str
idx = idx + 1
return email_cnt, idx | python | def __get_post_review(email_cnt, idx):
'''
Review for posts.
'''
for key in router_post:
recent_posts = MPost.query_recent_edited(tools.timestamp() - TIME_LIMIT, kind=key)
for recent_post in recent_posts:
hist_rec = MPostHist.get_last(recent_post.uid)
if hist_rec:
foo_str = '''
<tr><td>{0}</td><td>{1}</td><td class="diff_chg">Edit</td><td>{2}</td>
<td><a href="{3}">{3}</a></td></tr>
'''.format(idx, recent_post.user_name, recent_post.title,
os.path.join(SITE_CFG['site_url'], router_post[key],
recent_post.uid))
email_cnt = email_cnt + foo_str
else:
foo_str = '''
<tr><td>{0}</td><td>{1}</td><td class="diff_add">New </td><td>{2}</td>
<td><a href="{3}">{3}</a></td></tr>
'''.format(idx, recent_post.user_name, recent_post.title,
os.path.join(SITE_CFG['site_url'], router_post[key],
recent_post.uid))
email_cnt = email_cnt + foo_str
idx = idx + 1
return email_cnt, idx | Review for posts. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_review.py#L105-L131 |
bukun/TorCMS | torcms/script/script_review.py | run_review | def run_review(*args):
'''
Get the difference of recents modification, and send the Email.
For: wiki, page, and post.
'''
email_cnt = '''<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title></title>
<style type="text/css">
table.diff {font-family:Courier; border:medium;}
.diff_header {background-color:#e0e0e0}
td.diff_header {text-align:right}
.diff_next {background-color:#c0c0c0}
.diff_add {background-color:#aaffaa}
.diff_chg {background-color:#ffff77}
.diff_sub {background-color:#ffaaaa}
</style></head><body>'''
idx = 1
email_cnt = email_cnt + '<table border=1>'
email_cnt, idx = __get_post_review(email_cnt, idx) # post
email_cnt, idx = __get_page_review(email_cnt, idx) # page.
email_cnt, idx = __get_wiki_review(email_cnt, idx) # wiki
###########################################################
diff_str = __get_diff_recent()
if len(diff_str) < 20000:
email_cnt = email_cnt + diff_str
email_cnt = email_cnt + '''</body></html>'''
if idx > 1:
send_mail(post_emails, "{0}|{1}|{2}".format(SMTP_CFG['name'], '文档更新情况', DATE_STR), email_cnt) | python | def run_review(*args):
'''
Get the difference of recents modification, and send the Email.
For: wiki, page, and post.
'''
email_cnt = '''<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title></title>
<style type="text/css">
table.diff {font-family:Courier; border:medium;}
.diff_header {background-color:#e0e0e0}
td.diff_header {text-align:right}
.diff_next {background-color:#c0c0c0}
.diff_add {background-color:#aaffaa}
.diff_chg {background-color:#ffff77}
.diff_sub {background-color:#ffaaaa}
</style></head><body>'''
idx = 1
email_cnt = email_cnt + '<table border=1>'
email_cnt, idx = __get_post_review(email_cnt, idx) # post
email_cnt, idx = __get_page_review(email_cnt, idx) # page.
email_cnt, idx = __get_wiki_review(email_cnt, idx) # wiki
###########################################################
diff_str = __get_diff_recent()
if len(diff_str) < 20000:
email_cnt = email_cnt + diff_str
email_cnt = email_cnt + '''</body></html>'''
if idx > 1:
send_mail(post_emails, "{0}|{1}|{2}".format(SMTP_CFG['name'], '文档更新情况', DATE_STR), email_cnt) | Get the difference of recents modification, and send the Email.
For: wiki, page, and post. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_review.py#L134-L168 |
bukun/TorCMS | torcms/model/category_model.py | MCategory.get_qian2 | def get_qian2(qian2):
'''
用于首页。根据前两位，找到所有的大类与小类。
:param qian2: 分类id的前两位
:return: 数组，包含了找到的分类
'''
return TabTag.select().where(
TabTag.uid.startswith(qian2)
).order_by(TabTag.order) | python | def get_qian2(qian2):
'''
用于首页。根据前两位，找到所有的大类与小类。
:param qian2: 分类id的前两位
:return: 数组，包含了找到的分类
'''
return TabTag.select().where(
TabTag.uid.startswith(qian2)
).order_by(TabTag.order) | 用于首页。根据前两位，找到所有的大类与小类。
:param qian2: 分类id的前两位
:return: 数组，包含了找到的分类 | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/category_model.py#L31-L39 |
bukun/TorCMS | torcms/model/category_model.py | MCategory.query_all | def query_all(kind='1', by_count=False, by_order=True):
'''
Qeury all the categories, order by count or defined order.
'''
if by_count:
recs = TabTag.select().where(TabTag.kind == kind).order_by(TabTag.count.desc())
elif by_order:
recs = TabTag.select().where(TabTag.kind == kind).order_by(TabTag.order)
else:
recs = TabTag.select().where(TabTag.kind == kind).order_by(TabTag.uid)
return recs | python | def query_all(kind='1', by_count=False, by_order=True):
'''
Qeury all the categories, order by count or defined order.
'''
if by_count:
recs = TabTag.select().where(TabTag.kind == kind).order_by(TabTag.count.desc())
elif by_order:
recs = TabTag.select().where(TabTag.kind == kind).order_by(TabTag.order)
else:
recs = TabTag.select().where(TabTag.kind == kind).order_by(TabTag.uid)
return recs | Qeury all the categories, order by count or defined order. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/category_model.py#L69-L79 |
bukun/TorCMS | torcms/model/category_model.py | MCategory.query_field_count | def query_field_count(limit_num, kind='1'):
'''
Query the posts count of certain category.
'''
return TabTag.select().where(
TabTag.kind == kind
).order_by(
TabTag.count.desc()
).limit(limit_num) | python | def query_field_count(limit_num, kind='1'):
'''
Query the posts count of certain category.
'''
return TabTag.select().where(
TabTag.kind == kind
).order_by(
TabTag.count.desc()
).limit(limit_num) | Query the posts count of certain category. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/category_model.py#L82-L90 |
bukun/TorCMS | torcms/model/category_model.py | MCategory.get_by_slug | def get_by_slug(slug):
'''
return the category record .
'''
rec = TabTag.select().where(TabTag.slug == slug)
if rec.count() > 0:
return rec.get()
return None | python | def get_by_slug(slug):
'''
return the category record .
'''
rec = TabTag.select().where(TabTag.slug == slug)
if rec.count() > 0:
return rec.get()
return None | return the category record . | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/category_model.py#L93-L100 |
bukun/TorCMS | torcms/model/category_model.py | MCategory.update_count | def update_count(cat_id):
'''
Update the count of certain category.
'''
# Todo: the record not valid should not be counted.
entry2 = TabTag.update(
count=TabPost2Tag.select().where(
TabPost2Tag.tag_id == cat_id
).count()
).where(TabTag.uid == cat_id)
entry2.execute() | python | def update_count(cat_id):
'''
Update the count of certain category.
'''
# Todo: the record not valid should not be counted.
entry2 = TabTag.update(
count=TabPost2Tag.select().where(
TabPost2Tag.tag_id == cat_id
).count()
).where(TabTag.uid == cat_id)
entry2.execute() | Update the count of certain category. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/category_model.py#L103-L113 |
bukun/TorCMS | torcms/model/category_model.py | MCategory.update | def update(uid, post_data):
'''
Update the category.
'''
raw_rec = TabTag.get(TabTag.uid == uid)
entry = TabTag.update(
name=post_data['name'] if 'name' in post_data else raw_rec.name,
slug=post_data['slug'] if 'slug' in post_data else raw_rec.slug,
order=post_data['order'] if 'order' in post_data else raw_rec.order,
kind=post_data['kind'] if 'kind' in post_data else raw_rec.kind,
pid=post_data['pid'],
).where(TabTag.uid == uid)
entry.execute() | python | def update(uid, post_data):
'''
Update the category.
'''
raw_rec = TabTag.get(TabTag.uid == uid)
entry = TabTag.update(
name=post_data['name'] if 'name' in post_data else raw_rec.name,
slug=post_data['slug'] if 'slug' in post_data else raw_rec.slug,
order=post_data['order'] if 'order' in post_data else raw_rec.order,
kind=post_data['kind'] if 'kind' in post_data else raw_rec.kind,
pid=post_data['pid'],
).where(TabTag.uid == uid)
entry.execute() | Update the category. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/category_model.py#L116-L128 |
bukun/TorCMS | torcms/model/category_model.py | MCategory.add_or_update | def add_or_update(uid, post_data):
'''
Add or update the data by the given ID of post.
'''
catinfo = MCategory.get_by_uid(uid)
if catinfo:
MCategory.update(uid, post_data)
else:
TabTag.create(
uid=uid,
name=post_data['name'],
slug=post_data['slug'],
order=post_data['order'],
kind=post_data['kind'] if 'kind' in post_data else '1',
pid=post_data['pid'],
)
return uid | python | def add_or_update(uid, post_data):
'''
Add or update the data by the given ID of post.
'''
catinfo = MCategory.get_by_uid(uid)
if catinfo:
MCategory.update(uid, post_data)
else:
TabTag.create(
uid=uid,
name=post_data['name'],
slug=post_data['slug'],
order=post_data['order'],
kind=post_data['kind'] if 'kind' in post_data else '1',
pid=post_data['pid'],
)
return uid | Add or update the data by the given ID of post. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/category_model.py#L131-L147 |
bukun/TorCMS | torcms/handlers/category_handler.py | CategoryAjaxHandler.list_catalog | def list_catalog(self, kind):
'''
listing the category.
'''
kwd = {
'pager': '',
'title': '最近文档',
'kind': kind,
'router': config.router_post[kind]
}
self.render('admin/{0}/category_list.html'.format(self.tmpl_router),
kwd=kwd,
view=MCategory.query_all(kind, by_order=True),
format_date=tools.format_date,
userinfo=self.userinfo,
cfg=config.CMS_CFG) | python | def list_catalog(self, kind):
'''
listing the category.
'''
kwd = {
'pager': '',
'title': '最近文档',
'kind': kind,
'router': config.router_post[kind]
}
self.render('admin/{0}/category_list.html'.format(self.tmpl_router),
kwd=kwd,
view=MCategory.query_all(kind, by_order=True),
format_date=tools.format_date,
userinfo=self.userinfo,
cfg=config.CMS_CFG) | listing the category. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/category_handler.py#L38-L53 |
bukun/TorCMS | torcms/handlers/post_list_handler.py | PostListHandler.recent | def recent(self, with_catalog=True, with_date=True):
'''
List posts that recent edited.
'''
kwd = {
'pager': '',
'title': 'Recent posts.',
'with_catalog': with_catalog,
'with_date': with_date,
}
self.render('list/post_list.html',
kwd=kwd,
view=MPost.query_recent(num=20),
postrecs=MPost.query_recent(num=2),
format_date=tools.format_date,
userinfo=self.userinfo,
cfg=CMS_CFG, ) | python | def recent(self, with_catalog=True, with_date=True):
'''
List posts that recent edited.
'''
kwd = {
'pager': '',
'title': 'Recent posts.',
'with_catalog': with_catalog,
'with_date': with_date,
}
self.render('list/post_list.html',
kwd=kwd,
view=MPost.query_recent(num=20),
postrecs=MPost.query_recent(num=2),
format_date=tools.format_date,
userinfo=self.userinfo,
cfg=CMS_CFG, ) | List posts that recent edited. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/post_list_handler.py#L40-L56 |
bukun/TorCMS | torcms/handlers/post_list_handler.py | PostListHandler.errcat | def errcat(self):
'''
List the posts to be modified.
'''
post_recs = MPost.query_random(limit=1000)
outrecs = []
errrecs = []
idx = 0
for postinfo in post_recs:
if idx > 16:
break
cat = MPost2Catalog.get_first_category(postinfo.uid)
if cat:
if 'def_cat_uid' in postinfo.extinfo:
if postinfo.extinfo['def_cat_uid'] == cat.tag_id:
pass
else:
errrecs.append(postinfo)
idx += 1
else:
errrecs.append(postinfo)
idx += 1
else:
outrecs.append(postinfo)
idx += 1
self.render('list/errcat.html',
kwd={},
norecs=outrecs,
errrecs=errrecs,
userinfo=self.userinfo) | python | def errcat(self):
'''
List the posts to be modified.
'''
post_recs = MPost.query_random(limit=1000)
outrecs = []
errrecs = []
idx = 0
for postinfo in post_recs:
if idx > 16:
break
cat = MPost2Catalog.get_first_category(postinfo.uid)
if cat:
if 'def_cat_uid' in postinfo.extinfo:
if postinfo.extinfo['def_cat_uid'] == cat.tag_id:
pass
else:
errrecs.append(postinfo)
idx += 1
else:
errrecs.append(postinfo)
idx += 1
else:
outrecs.append(postinfo)
idx += 1
self.render('list/errcat.html',
kwd={},
norecs=outrecs,
errrecs=errrecs,
userinfo=self.userinfo) | List the posts to be modified. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/post_list_handler.py#L58-L87 |
bukun/TorCMS | torcms/handlers/post_list_handler.py | PostListHandler.refresh | def refresh(self):
'''
List the post of dated.
'''
kwd = {
'pager': '',
'title': '',
}
self.render('list/post_list.html',
kwd=kwd,
userinfo=self.userinfo,
view=MPost.query_dated(10),
postrecs=MPost.query_dated(10),
format_date=tools.format_date,
cfg=CMS_CFG) | python | def refresh(self):
'''
List the post of dated.
'''
kwd = {
'pager': '',
'title': '',
}
self.render('list/post_list.html',
kwd=kwd,
userinfo=self.userinfo,
view=MPost.query_dated(10),
postrecs=MPost.query_dated(10),
format_date=tools.format_date,
cfg=CMS_CFG) | List the post of dated. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/handlers/post_list_handler.py#L89-L103 |
bukun/TorCMS | torcms/script/autocrud/base_crud.py | build_dir | def build_dir():
'''
Build the directory used for templates.
'''
tag_arr = ['add', 'edit', 'view', 'list', 'infolist']
path_arr = [os.path.join(CRUD_PATH, x) for x in tag_arr]
for wpath in path_arr:
if os.path.exists(wpath):
continue
os.makedirs(wpath) | python | def build_dir():
'''
Build the directory used for templates.
'''
tag_arr = ['add', 'edit', 'view', 'list', 'infolist']
path_arr = [os.path.join(CRUD_PATH, x) for x in tag_arr]
for wpath in path_arr:
if os.path.exists(wpath):
continue
os.makedirs(wpath) | Build the directory used for templates. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/autocrud/base_crud.py#L31-L40 |
bukun/TorCMS | torcms/model/reply_model.py | MReply.create_reply | def create_reply(post_data):
'''
Create the reply.
'''
uid = tools.get_uuid()
TabReply.create(
uid=uid,
post_id=post_data['post_id'],
user_name=post_data['user_name'],
user_id=post_data['user_id'],
timestamp=tools.timestamp(),
date=datetime.datetime.now(),
cnt_md=tornado.escape.xhtml_escape(post_data['cnt_reply']),
cnt_html=tools.markdown2html(post_data['cnt_reply']),
vote=0
)
return uid | python | def create_reply(post_data):
'''
Create the reply.
'''
uid = tools.get_uuid()
TabReply.create(
uid=uid,
post_id=post_data['post_id'],
user_name=post_data['user_name'],
user_id=post_data['user_id'],
timestamp=tools.timestamp(),
date=datetime.datetime.now(),
cnt_md=tornado.escape.xhtml_escape(post_data['cnt_reply']),
cnt_html=tools.markdown2html(post_data['cnt_reply']),
vote=0
)
return uid | Create the reply. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/reply_model.py#L33-L49 |
bukun/TorCMS | torcms/model/reply_model.py | MReply.query_by_post | def query_by_post(postid):
'''
Get reply list of certain post.
'''
return TabReply.select().where(
TabReply.post_id == postid
).order_by(TabReply.timestamp.desc()) | python | def query_by_post(postid):
'''
Get reply list of certain post.
'''
return TabReply.select().where(
TabReply.post_id == postid
).order_by(TabReply.timestamp.desc()) | Get reply list of certain post. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/reply_model.py#L52-L58 |
bukun/TorCMS | ext_script/autocrud/fetch_html_dic.py | __write_filter_dic | def __write_filter_dic(wk_sheet, column):
'''
return filter dic for certain column
'''
row1_val = wk_sheet['{0}1'.format(column)].value
row2_val = wk_sheet['{0}2'.format(column)].value
row3_val = wk_sheet['{0}3'.format(column)].value
row4_val = wk_sheet['{0}4'.format(column)].value
if row1_val and row1_val.strip() != '':
row2_val = row2_val.strip()
slug_name = row1_val.strip()
c_name = row2_val.strip()
tags1 = [x.strip() for x in row3_val.split(',')]
tags_dic = {}
# if only one tag,
if len(tags1) == 1:
xx_1 = row2_val.split(':') # 'text' # HTML text input control.
if xx_1[0].lower() in INPUT_ARR:
xx_1[0] = xx_1[0].lower()
else:
xx_1[0] = 'text'
if len(xx_1) == 2:
ctr_type, unit = xx_1
else:
ctr_type = xx_1[0]
unit = ''
tags_dic[1] = unit
else:
ctr_type = 'select' # HTML selectiom control.
for index, tag_val in enumerate(tags1):
# the index of tags_dic starts from 1.
tags_dic[index + 1] = tag_val.strip()
outkey = 'html_{0}'.format(slug_name)
outval = {
'en': slug_name,
'zh': c_name,
'dic': tags_dic,
'type': ctr_type,
'display': row4_val,
}
return (outkey, outval)
else:
return (None, None) | python | def __write_filter_dic(wk_sheet, column):
'''
return filter dic for certain column
'''
row1_val = wk_sheet['{0}1'.format(column)].value
row2_val = wk_sheet['{0}2'.format(column)].value
row3_val = wk_sheet['{0}3'.format(column)].value
row4_val = wk_sheet['{0}4'.format(column)].value
if row1_val and row1_val.strip() != '':
row2_val = row2_val.strip()
slug_name = row1_val.strip()
c_name = row2_val.strip()
tags1 = [x.strip() for x in row3_val.split(',')]
tags_dic = {}
# if only one tag,
if len(tags1) == 1:
xx_1 = row2_val.split(':') # 'text' # HTML text input control.
if xx_1[0].lower() in INPUT_ARR:
xx_1[0] = xx_1[0].lower()
else:
xx_1[0] = 'text'
if len(xx_1) == 2:
ctr_type, unit = xx_1
else:
ctr_type = xx_1[0]
unit = ''
tags_dic[1] = unit
else:
ctr_type = 'select' # HTML selectiom control.
for index, tag_val in enumerate(tags1):
# the index of tags_dic starts from 1.
tags_dic[index + 1] = tag_val.strip()
outkey = 'html_{0}'.format(slug_name)
outval = {
'en': slug_name,
'zh': c_name,
'dic': tags_dic,
'type': ctr_type,
'display': row4_val,
}
return (outkey, outval)
else:
return (None, None) | return filter dic for certain column | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/ext_script/autocrud/fetch_html_dic.py#L17-L67 |
bukun/TorCMS | torcms/model/wiki_hist_model.py | MWikiHist.get_last | def get_last(postid):
'''
Get the last wiki in history.
'''
recs = TabWikiHist.select().where(
TabWikiHist.wiki_id == postid
).order_by(TabWikiHist.time_update.desc())
return None if recs.count() == 0 else recs.get() | python | def get_last(postid):
'''
Get the last wiki in history.
'''
recs = TabWikiHist.select().where(
TabWikiHist.wiki_id == postid
).order_by(TabWikiHist.time_update.desc())
return None if recs.count() == 0 else recs.get() | Get the last wiki in history. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/model/wiki_hist_model.py#L11-L19 |
bukun/TorCMS | torcms/core/privilege.py | is_prived | def is_prived(usr_rule, def_rule):
'''
Compare between two role string.
'''
for iii in range(4):
if def_rule[iii] == '0':
continue
if usr_rule[iii] >= def_rule[iii]:
return True
return False | python | def is_prived(usr_rule, def_rule):
'''
Compare between two role string.
'''
for iii in range(4):
if def_rule[iii] == '0':
continue
if usr_rule[iii] >= def_rule[iii]:
return True
return False | Compare between two role string. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/core/privilege.py#L10-L20 |
bukun/TorCMS | torcms/core/privilege.py | auth_view | def auth_view(method):
'''
role for view.
'''
def wrapper(self, *args, **kwargs):
'''
wrapper.
'''
if ROLE_CFG['view'] == '':
return method(self, *args, **kwargs)
elif self.current_user:
if is_prived(self.userinfo.role, ROLE_CFG['view']):
return method(self, *args, **kwargs)
else:
kwd = {
'info': 'No role',
}
self.render('misc/html/404.html',
kwd=kwd,
userinfo=self.userinfo)
else:
kwd = {
'info': 'No role',
}
self.render('misc/html/404.html',
kwd=kwd,
userinfo=self.userinfo)
return wrapper | python | def auth_view(method):
'''
role for view.
'''
def wrapper(self, *args, **kwargs):
'''
wrapper.
'''
if ROLE_CFG['view'] == '':
return method(self, *args, **kwargs)
elif self.current_user:
if is_prived(self.userinfo.role, ROLE_CFG['view']):
return method(self, *args, **kwargs)
else:
kwd = {
'info': 'No role',
}
self.render('misc/html/404.html',
kwd=kwd,
userinfo=self.userinfo)
else:
kwd = {
'info': 'No role',
}
self.render('misc/html/404.html',
kwd=kwd,
userinfo=self.userinfo)
return wrapper | role for view. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/core/privilege.py#L23-L54 |
bukun/TorCMS | torcms/script/script_drop_tabels.py | run_drop_tables | def run_drop_tables(_):
'''
Running the script.
'''
print('--')
drop_the_table(TabPost)
drop_the_table(TabTag)
drop_the_table(TabMember)
drop_the_table(TabWiki)
drop_the_table(TabLink)
drop_the_table(TabEntity)
drop_the_table(TabPostHist)
drop_the_table(TabWikiHist)
drop_the_table(TabCollect)
drop_the_table(TabPost2Tag)
drop_the_table(TabRel)
drop_the_table(TabEvaluation)
drop_the_table(TabUsage)
drop_the_table(TabReply)
drop_the_table(TabUser2Reply)
drop_the_table(TabRating) | python | def run_drop_tables(_):
'''
Running the script.
'''
print('--')
drop_the_table(TabPost)
drop_the_table(TabTag)
drop_the_table(TabMember)
drop_the_table(TabWiki)
drop_the_table(TabLink)
drop_the_table(TabEntity)
drop_the_table(TabPostHist)
drop_the_table(TabWikiHist)
drop_the_table(TabCollect)
drop_the_table(TabPost2Tag)
drop_the_table(TabRel)
drop_the_table(TabEvaluation)
drop_the_table(TabUsage)
drop_the_table(TabReply)
drop_the_table(TabUser2Reply)
drop_the_table(TabRating) | Running the script. | https://github.com/bukun/TorCMS/blob/6567c7fe2604a1d646d4570c017840958630ed2b/torcms/script/script_drop_tabels.py#L22-L43 |
shon/httpagentparser | httpagentparser/__init__.py | detect | def detect(agent, fill_none=False):
"""
fill_none: if name/version is not detected respective key is still added to the result with value None
"""
result = dict(platform=dict(name=None, version=None))
_suggested_detectors = []
for info_type in detectorshub:
detectors = _suggested_detectors or detectorshub[info_type]
for detector in detectors:
try:
detector.detect(agent, result)
except Exception as _err:
pass
if fill_none:
for outer_key in ('os', 'browser'):
outer_value = result.setdefault(outer_key, dict())
for inner_key in ('name', 'version'):
outer_value.setdefault(inner_key, None)
return result | python | def detect(agent, fill_none=False):
"""
fill_none: if name/version is not detected respective key is still added to the result with value None
"""
result = dict(platform=dict(name=None, version=None))
_suggested_detectors = []
for info_type in detectorshub:
detectors = _suggested_detectors or detectorshub[info_type]
for detector in detectors:
try:
detector.detect(agent, result)
except Exception as _err:
pass
if fill_none:
for outer_key in ('os', 'browser'):
outer_value = result.setdefault(outer_key, dict())
for inner_key in ('name', 'version'):
outer_value.setdefault(inner_key, None)
return result | fill_none: if name/version is not detected respective key is still added to the result with value None | https://github.com/shon/httpagentparser/blob/c08489408a9b9e67c83eb850d15e108c5270c97f/httpagentparser/__init__.py#L637-L658 |
shon/httpagentparser | httpagentparser/__init__.py | simple_detect | def simple_detect(agent):
"""
-> (os, browser) # tuple of strings
"""
result = detect(agent)
os_list = []
if 'flavor' in result:
os_list.append(result['flavor']['name'])
if 'dist' in result:
os_list.append(result['dist']['name'])
if 'os' in result:
os_list.append(result['os']['name'])
os = os_list and " ".join(os_list) or "Unknown OS"
os_version = os_list and (result.get('flavor') and result['flavor'].get('version')) or \
(result.get('dist') and result['dist'].get('version')) or (result.get('os') and result['os'].get('version')) or ""
browser = 'browser' in result and result['browser'].get('name') or 'Unknown Browser'
browser_version = 'browser' in result and result['browser'].get('version') or ""
if browser_version:
browser = " ".join((browser, browser_version))
if os_version:
os = " ".join((os, os_version))
return os, browser | python | def simple_detect(agent):
"""
-> (os, browser) # tuple of strings
"""
result = detect(agent)
os_list = []
if 'flavor' in result:
os_list.append(result['flavor']['name'])
if 'dist' in result:
os_list.append(result['dist']['name'])
if 'os' in result:
os_list.append(result['os']['name'])
os = os_list and " ".join(os_list) or "Unknown OS"
os_version = os_list and (result.get('flavor') and result['flavor'].get('version')) or \
(result.get('dist') and result['dist'].get('version')) or (result.get('os') and result['os'].get('version')) or ""
browser = 'browser' in result and result['browser'].get('name') or 'Unknown Browser'
browser_version = 'browser' in result and result['browser'].get('version') or ""
if browser_version:
browser = " ".join((browser, browser_version))
if os_version:
os = " ".join((os, os_version))
return os, browser | -> (os, browser) # tuple of strings | https://github.com/shon/httpagentparser/blob/c08489408a9b9e67c83eb850d15e108c5270c97f/httpagentparser/__init__.py#L661-L683 |
shon/httpagentparser | httpagentparser/__init__.py | DetectorBase.getVersion | def getVersion(self, agent, word):
"""
=> version string /None
"""
version_markers = self.version_markers if \
isinstance(self.version_markers[0], (list, tuple)) else [self.version_markers]
version_part = agent.split(word, 1)[-1]
for start, end in version_markers:
if version_part.startswith(start) and end in version_part:
version = version_part[1:]
if end: # end could be empty string
version = version.split(end)[0]
if not self.allow_space_in_version:
version = version.split()[0]
return version | python | def getVersion(self, agent, word):
"""
=> version string /None
"""
version_markers = self.version_markers if \
isinstance(self.version_markers[0], (list, tuple)) else [self.version_markers]
version_part = agent.split(word, 1)[-1]
for start, end in version_markers:
if version_part.startswith(start) and end in version_part:
version = version_part[1:]
if end: # end could be empty string
version = version.split(end)[0]
if not self.allow_space_in_version:
version = version.split()[0]
return version | => version string /None | https://github.com/shon/httpagentparser/blob/c08489408a9b9e67c83eb850d15e108c5270c97f/httpagentparser/__init__.py#L84-L98 |
ylogx/universal | universal/builder.py | compile_files | def compile_files(args, mem_test=False):
''' Copiles the files and runs memory tests
if needed.
PARAM args: list of files passed as CMD args
to be compiled.
PARAM mem_test: Weither to perform memory test ?
'''
for filename in args:
if not os.path.isfile(filename):
print('The file doesn\'t exits')
return
build_and_run_file(filename)
print("") | python | def compile_files(args, mem_test=False):
''' Copiles the files and runs memory tests
if needed.
PARAM args: list of files passed as CMD args
to be compiled.
PARAM mem_test: Weither to perform memory test ?
'''
for filename in args:
if not os.path.isfile(filename):
print('The file doesn\'t exits')
return
build_and_run_file(filename)
print("") | Copiles the files and runs memory tests
if needed.
PARAM args: list of files passed as CMD args
to be compiled.
PARAM mem_test: Weither to perform memory test ? | https://github.com/ylogx/universal/blob/1be04c2e828d9f97a94d48bff64031b14c2b8463/universal/builder.py#L41-L53 |
ylogx/universal | universal/builder.py | build_and_run_file | def build_and_run_file(filename):
''' Builds and runs the filename specified
according to the extension
PARAM filename: name of file to build and run
'''
(directory, name, extension) = get_file_tuple(filename)
if extension == 'c':
print(" = = = = = = ", YELLOW, "GCC: Compiling " + filename + " file", \
RESET, " = = = = = =\n")
compiler = Compiler(filename)
out = compiler.compile()
if out != 0:
print('Error while compiling. Code:', out, 'Please retry.')
return out
print("")
out = compiler.run()
return out
elif extension == 'cpp':
print(" = = = = = = ", YELLOW, "GPP: Compiling " + filename + " file", \
RESET, " = = = = = =\n")
compiler = Compiler(filename)
out = compiler.compile()
if out != 0:
print('Error while compiling. Code:', out, 'Please retry.')
return out
print("")
out = compiler.run()
return out
elif extension == 'py':
print(" = = = = = = ", YELLOW, "PYTHON: Executing " + filename + " file", \
RESET, " = = = = = =\n")
compiler = Compiler(filename)
out = compiler.run()
return out
elif extension == 'java':
command = EXECUTABLE_JAVAC + ' ' + filename
perform_system_command(command)
command_run = EXECUTABLE_JAVA + ' ' + name
test_file = directory + "/" + name + ".input"
if os.path.exists(test_file):
command_run += " < " + test_file
return perform_system_command(command_run)
else:
print("Language yet not supported")
return -1 | python | def build_and_run_file(filename):
''' Builds and runs the filename specified
according to the extension
PARAM filename: name of file to build and run
'''
(directory, name, extension) = get_file_tuple(filename)
if extension == 'c':
print(" = = = = = = ", YELLOW, "GCC: Compiling " + filename + " file", \
RESET, " = = = = = =\n")
compiler = Compiler(filename)
out = compiler.compile()
if out != 0:
print('Error while compiling. Code:', out, 'Please retry.')
return out
print("")
out = compiler.run()
return out
elif extension == 'cpp':
print(" = = = = = = ", YELLOW, "GPP: Compiling " + filename + " file", \
RESET, " = = = = = =\n")
compiler = Compiler(filename)
out = compiler.compile()
if out != 0:
print('Error while compiling. Code:', out, 'Please retry.')
return out
print("")
out = compiler.run()
return out
elif extension == 'py':
print(" = = = = = = ", YELLOW, "PYTHON: Executing " + filename + " file", \
RESET, " = = = = = =\n")
compiler = Compiler(filename)
out = compiler.run()
return out
elif extension == 'java':
command = EXECUTABLE_JAVAC + ' ' + filename
perform_system_command(command)
command_run = EXECUTABLE_JAVA + ' ' + name
test_file = directory + "/" + name + ".input"
if os.path.exists(test_file):
command_run += " < " + test_file
return perform_system_command(command_run)
else:
print("Language yet not supported")
return -1 | Builds and runs the filename specified
according to the extension
PARAM filename: name of file to build and run | https://github.com/ylogx/universal/blob/1be04c2e828d9f97a94d48bff64031b14c2b8463/universal/builder.py#L56-L104 |
ylogx/universal | universal/util.py | check_exec_installed | def check_exec_installed(exec_list):
""" Check the required programs are
installed.
PARAM exec_list: list of programs to check
RETURN: True if all installed else False
"""
all_installed = True
for exe in exec_list:
if not is_tool(exe):
print("Executable: " + exe + " is not installed")
all_installed = False
return all_installed | python | def check_exec_installed(exec_list):
""" Check the required programs are
installed.
PARAM exec_list: list of programs to check
RETURN: True if all installed else False
"""
all_installed = True
for exe in exec_list:
if not is_tool(exe):
print("Executable: " + exe + " is not installed")
all_installed = False
return all_installed | Check the required programs are
installed.
PARAM exec_list: list of programs to check
RETURN: True if all installed else False | https://github.com/ylogx/universal/blob/1be04c2e828d9f97a94d48bff64031b14c2b8463/universal/util.py#L23-L34 |
ylogx/universal | universal/main.py | parse_known_args | def parse_known_args():
""" Parse command line arguments
"""
parser = ArgumentParser()
parser.add_argument("-l", "--loop", type=int, help="Loop every X seconds")
parser.add_argument('-V', '--version',
action='store_true',
dest='version',
help='Print the version number and exit')
parser.add_argument("-u", "--update",
action='store_true',
dest="update",
help="Update the software from online repo")
parser.add_argument("-p", "--problem",
action='store_true',
dest="problem",
help="Report a problem")
parser.add_argument("-m", "--memory",
action='store_true',
dest="memory",
help="Run memory tests")
args, otherthings = parser.parse_known_args()
return args, otherthings, parser | python | def parse_known_args():
""" Parse command line arguments
"""
parser = ArgumentParser()
parser.add_argument("-l", "--loop", type=int, help="Loop every X seconds")
parser.add_argument('-V', '--version',
action='store_true',
dest='version',
help='Print the version number and exit')
parser.add_argument("-u", "--update",
action='store_true',
dest="update",
help="Update the software from online repo")
parser.add_argument("-p", "--problem",
action='store_true',
dest="problem",
help="Report a problem")
parser.add_argument("-m", "--memory",
action='store_true',
dest="memory",
help="Run memory tests")
args, otherthings = parser.parse_known_args()
return args, otherthings, parser | Parse command line arguments | https://github.com/ylogx/universal/blob/1be04c2e828d9f97a94d48bff64031b14c2b8463/universal/main.py#L43-L65 |
xieqihui/pandas-multiprocess | pandas_multiprocess/multiprocess.py | multi_process | def multi_process(func, data, num_process=None, verbose=True, **args):
'''Function to use multiprocessing to process pandas Dataframe.
This function applies a function on each row of the input DataFrame by
multiprocessing.
Args:
func (function): The function to apply on each row of the input
Dataframe. The func must accept pandas.Series as the first
positional argument and return a pandas.Series.
data (pandas.DataFrame): A DataFrame to be processed.
num_process (int, optional): The number of processes to run in
parallel. Defaults to be the number of CPUs of the computer.
verbose (bool, optional): Set to False to disable verbose output.
args (dict): Keyword arguments to pass as keywords arguments to `func`
return:
A dataframe containing the results
'''
# Check arguments value
assert isinstance(data, pd.DataFrame), \
'Input data must be a pandas.DataFrame instance'
if num_process is None:
num_process = multiprocessing.cpu_count()
# Establish communication queues
tasks = multiprocessing.JoinableQueue()
results = multiprocessing.Queue()
error_queue = multiprocessing.Queue()
start_time = time.time()
# Enqueue tasks
num_task = len(data)
for i in range(num_task):
tasks.put(data.iloc[i, :])
# Add a poison pill for each consumer
for i in range(num_process):
tasks.put(None)
logger.info('Create {} processes'.format(num_process))
consumers = [Consumer(func, tasks, results, error_queue, **args)
for i in range(num_process)]
for w in consumers:
w.start()
# Add a task tracking process
task_tracker = TaskTracker(tasks, verbose)
task_tracker.start()
# Wait for all input data to be processed
tasks.join()
# If there is any error in any process, output the error messages
num_error = error_queue.qsize()
if num_error > 0:
for i in range(num_error):
logger.error(error_queue.get())
raise RuntimeError('Multi process jobs failed')
else:
# Collect results
result_table = []
while num_task:
result_table.append(results.get())
num_task -= 1
df_results = pd.DataFrame(result_table)
logger.info("Jobs finished in {0:.2f}s".format(
time.time()-start_time))
return df_results | python | def multi_process(func, data, num_process=None, verbose=True, **args):
'''Function to use multiprocessing to process pandas Dataframe.
This function applies a function on each row of the input DataFrame by
multiprocessing.
Args:
func (function): The function to apply on each row of the input
Dataframe. The func must accept pandas.Series as the first
positional argument and return a pandas.Series.
data (pandas.DataFrame): A DataFrame to be processed.
num_process (int, optional): The number of processes to run in
parallel. Defaults to be the number of CPUs of the computer.
verbose (bool, optional): Set to False to disable verbose output.
args (dict): Keyword arguments to pass as keywords arguments to `func`
return:
A dataframe containing the results
'''
# Check arguments value
assert isinstance(data, pd.DataFrame), \
'Input data must be a pandas.DataFrame instance'
if num_process is None:
num_process = multiprocessing.cpu_count()
# Establish communication queues
tasks = multiprocessing.JoinableQueue()
results = multiprocessing.Queue()
error_queue = multiprocessing.Queue()
start_time = time.time()
# Enqueue tasks
num_task = len(data)
for i in range(num_task):
tasks.put(data.iloc[i, :])
# Add a poison pill for each consumer
for i in range(num_process):
tasks.put(None)
logger.info('Create {} processes'.format(num_process))
consumers = [Consumer(func, tasks, results, error_queue, **args)
for i in range(num_process)]
for w in consumers:
w.start()
# Add a task tracking process
task_tracker = TaskTracker(tasks, verbose)
task_tracker.start()
# Wait for all input data to be processed
tasks.join()
# If there is any error in any process, output the error messages
num_error = error_queue.qsize()
if num_error > 0:
for i in range(num_error):
logger.error(error_queue.get())
raise RuntimeError('Multi process jobs failed')
else:
# Collect results
result_table = []
while num_task:
result_table.append(results.get())
num_task -= 1
df_results = pd.DataFrame(result_table)
logger.info("Jobs finished in {0:.2f}s".format(
time.time()-start_time))
return df_results | Function to use multiprocessing to process pandas Dataframe.
This function applies a function on each row of the input DataFrame by
multiprocessing.
Args:
func (function): The function to apply on each row of the input
Dataframe. The func must accept pandas.Series as the first
positional argument and return a pandas.Series.
data (pandas.DataFrame): A DataFrame to be processed.
num_process (int, optional): The number of processes to run in
parallel. Defaults to be the number of CPUs of the computer.
verbose (bool, optional): Set to False to disable verbose output.
args (dict): Keyword arguments to pass as keywords arguments to `func`
return:
A dataframe containing the results | https://github.com/xieqihui/pandas-multiprocess/blob/b4d1b7357a446ded93183fb7b3e0d464ac7cc784/pandas_multiprocess/multiprocess.py#L125-L186 |
xieqihui/pandas-multiprocess | pandas_multiprocess/multiprocess.py | Consumer.run | def run(self):
'''Define the job of each process to run.
'''
while True:
next_task = self._task_queue.get()
# If there is any error, only consume data but not run the job
if self._error_queue.qsize() > 0:
self._task_queue.task_done()
continue
if next_task is None:
# Poison pill means shutdown
self._task_queue.task_done()
break
try:
answer = self._func(next_task, **self._args)
self._task_queue.task_done()
self._result_queue.put(answer)
except Exception as e:
self._task_queue.task_done()
self._error_queue.put((os.getpid(), e))
logger.error(e)
continue | python | def run(self):
'''Define the job of each process to run.
'''
while True:
next_task = self._task_queue.get()
# If there is any error, only consume data but not run the job
if self._error_queue.qsize() > 0:
self._task_queue.task_done()
continue
if next_task is None:
# Poison pill means shutdown
self._task_queue.task_done()
break
try:
answer = self._func(next_task, **self._args)
self._task_queue.task_done()
self._result_queue.put(answer)
except Exception as e:
self._task_queue.task_done()
self._error_queue.put((os.getpid(), e))
logger.error(e)
continue | Define the job of each process to run. | https://github.com/xieqihui/pandas-multiprocess/blob/b4d1b7357a446ded93183fb7b3e0d464ac7cc784/pandas_multiprocess/multiprocess.py#L57-L78 |
xieqihui/pandas-multiprocess | pandas_multiprocess/multiprocess.py | TaskTracker.run | def run(self):
'''Define the job of each process to run.
'''
if self.verbose:
pbar = tqdm(total=100)
while True:
task_remain = self._task_queue.qsize()
task_finished = int((float(self.total_task - task_remain) /
float(self.total_task)) * 100)
if task_finished % 20 == 0 and task_finished != self.current_state:
self.current_state = task_finished
logger.info('{0}% done'.format(task_finished))
if self.verbose and task_finished > 0:
pbar.update(20)
if task_remain == 0:
break
logger.debug('All task data cleared') | python | def run(self):
'''Define the job of each process to run.
'''
if self.verbose:
pbar = tqdm(total=100)
while True:
task_remain = self._task_queue.qsize()
task_finished = int((float(self.total_task - task_remain) /
float(self.total_task)) * 100)
if task_finished % 20 == 0 and task_finished != self.current_state:
self.current_state = task_finished
logger.info('{0}% done'.format(task_finished))
if self.verbose and task_finished > 0:
pbar.update(20)
if task_remain == 0:
break
logger.debug('All task data cleared') | Define the job of each process to run. | https://github.com/xieqihui/pandas-multiprocess/blob/b4d1b7357a446ded93183fb7b3e0d464ac7cc784/pandas_multiprocess/multiprocess.py#L106-L122 |
xieqihui/pandas-multiprocess | examples/example.py | func | def func(data_row, wait):
''' A sample function
It takes 'wait' seconds to calculate the sum of each row
'''
time.sleep(wait)
data_row['sum'] = data_row['col_1'] + data_row['col_2']
return data_row | python | def func(data_row, wait):
''' A sample function
It takes 'wait' seconds to calculate the sum of each row
'''
time.sleep(wait)
data_row['sum'] = data_row['col_1'] + data_row['col_2']
return data_row | A sample function
It takes 'wait' seconds to calculate the sum of each row | https://github.com/xieqihui/pandas-multiprocess/blob/b4d1b7357a446ded93183fb7b3e0d464ac7cc784/examples/example.py#L7-L13 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | me | def me(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the mean error of the simulated and observed data.
.. image:: /pictures/ME.png
**Range:** -inf < MAE < inf, data units, closer to zero is better, indicates bias.
**Notes:** The mean error (ME) measures the difference between the simulated data and the
observed data. For the mean error, a smaller number indicates a better fit to the original
data. Note that if the error is in the form of random noise, the mean error will be very small,
which can skew the accuracy of this metric. ME is cumulative and will be small even if there
are large positive and negative errors that balance.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean error value.
Examples
--------
Note that in this example the random noise cancels, leaving a very small ME.
>>> import HydroErr as he
>>> import numpy as np
>>> # Seed for reproducibility
>>> np.random.seed(54839)
>>> x = np.arange(100) / 20
>>> sim = np.sin(x) + 2
>>> obs = sim * (((np.random.rand(100) - 0.5) / 10) + 1)
>>> he.me(sim, obs)
-0.006832220968967168
References
----------
- Fisher, R.A., 1920. A Mathematical Examination of the Methods of Determining the Accuracy of
an Observation by the Mean Error, and by the Mean Square Error. Monthly Notices of the Royal
Astronomical Society 80 758 - 770.
"""
# Treating missing values
simulated_array, observed_array = treat_values(simulated_array, observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero)
return np.mean(simulated_array - observed_array) | python | def me(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the mean error of the simulated and observed data.
.. image:: /pictures/ME.png
**Range:** -inf < MAE < inf, data units, closer to zero is better, indicates bias.
**Notes:** The mean error (ME) measures the difference between the simulated data and the
observed data. For the mean error, a smaller number indicates a better fit to the original
data. Note that if the error is in the form of random noise, the mean error will be very small,
which can skew the accuracy of this metric. ME is cumulative and will be small even if there
are large positive and negative errors that balance.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean error value.
Examples
--------
Note that in this example the random noise cancels, leaving a very small ME.
>>> import HydroErr as he
>>> import numpy as np
>>> # Seed for reproducibility
>>> np.random.seed(54839)
>>> x = np.arange(100) / 20
>>> sim = np.sin(x) + 2
>>> obs = sim * (((np.random.rand(100) - 0.5) / 10) + 1)
>>> he.me(sim, obs)
-0.006832220968967168
References
----------
- Fisher, R.A., 1920. A Mathematical Examination of the Methods of Determining the Accuracy of
an Observation by the Mean Error, and by the Mean Square Error. Monthly Notices of the Royal
Astronomical Society 80 758 - 770.
"""
# Treating missing values
simulated_array, observed_array = treat_values(simulated_array, observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero)
return np.mean(simulated_array - observed_array) | Compute the mean error of the simulated and observed data.
.. image:: /pictures/ME.png
**Range:** -inf < MAE < inf, data units, closer to zero is better, indicates bias.
**Notes:** The mean error (ME) measures the difference between the simulated data and the
observed data. For the mean error, a smaller number indicates a better fit to the original
data. Note that if the error is in the form of random noise, the mean error will be very small,
which can skew the accuracy of this metric. ME is cumulative and will be small even if there
are large positive and negative errors that balance.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean error value.
Examples
--------
Note that in this example the random noise cancels, leaving a very small ME.
>>> import HydroErr as he
>>> import numpy as np
>>> # Seed for reproducibility
>>> np.random.seed(54839)
>>> x = np.arange(100) / 20
>>> sim = np.sin(x) + 2
>>> obs = sim * (((np.random.rand(100) - 0.5) / 10) + 1)
>>> he.me(sim, obs)
-0.006832220968967168
References
----------
- Fisher, R.A., 1920. A Mathematical Examination of the Methods of Determining the Accuracy of
an Observation by the Mean Error, and by the Mean Square Error. Monthly Notices of the Royal
Astronomical Society 80 758 - 770. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L39-L114 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | mae | def mae(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the mean absolute error of the simulated and observed data.
.. image:: /pictures/MAE.png
**Range:** 0 ≤ MAE < inf, data units, smaller is better.
**Notes:** The ME measures the absolute difference between the simulated data and the observed
data. For the mean abolute error, a smaller number indicates a better fit to the original data.
Also note that random errors do not cancel. Also referred to as an L1-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute error value.
References
----------
- Willmott, Cort J., and Kenji Matsuura. “Advantages of the Mean Absolute Error (MAE) over the
Root Mean Square Error (RMSE) in Assessing Average Model Performance.” Climate Research 30,
no. 1 (2005): 79–82.
- Willmott, Cort J., and Kenji Matsuura. “On the Use of Dimensioned Measures of Error to
Evaluate the Performance of Spatial Interpolators.” International Journal of Geographical
Information Science 20, no. 1 (2006): 89–102.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.mae(sim, obs)
0.5666666666666665
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
return np.mean(np.absolute(simulated_array - observed_array)) | python | def mae(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the mean absolute error of the simulated and observed data.
.. image:: /pictures/MAE.png
**Range:** 0 ≤ MAE < inf, data units, smaller is better.
**Notes:** The ME measures the absolute difference between the simulated data and the observed
data. For the mean abolute error, a smaller number indicates a better fit to the original data.
Also note that random errors do not cancel. Also referred to as an L1-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute error value.
References
----------
- Willmott, Cort J., and Kenji Matsuura. “Advantages of the Mean Absolute Error (MAE) over the
Root Mean Square Error (RMSE) in Assessing Average Model Performance.” Climate Research 30,
no. 1 (2005): 79–82.
- Willmott, Cort J., and Kenji Matsuura. “On the Use of Dimensioned Measures of Error to
Evaluate the Performance of Spatial Interpolators.” International Journal of Geographical
Information Science 20, no. 1 (2006): 89–102.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.mae(sim, obs)
0.5666666666666665
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
return np.mean(np.absolute(simulated_array - observed_array)) | Compute the mean absolute error of the simulated and observed data.
.. image:: /pictures/MAE.png
**Range:** 0 ≤ MAE < inf, data units, smaller is better.
**Notes:** The ME measures the absolute difference between the simulated data and the observed
data. For the mean abolute error, a smaller number indicates a better fit to the original data.
Also note that random errors do not cancel. Also referred to as an L1-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute error value.
References
----------
- Willmott, Cort J., and Kenji Matsuura. “Advantages of the Mean Absolute Error (MAE) over the
Root Mean Square Error (RMSE) in Assessing Average Model Performance.” Climate Research 30,
no. 1 (2005): 79–82.
- Willmott, Cort J., and Kenji Matsuura. “On the Use of Dimensioned Measures of Error to
Evaluate the Performance of Spatial Interpolators.” International Journal of Geographical
Information Science 20, no. 1 (2006): 89–102.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.mae(sim, obs)
0.5666666666666665 | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L117-L193 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | mle | def mle(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the mean log error of the simulated and observed data.
.. image:: /pictures/MLE.png
**Range:** -inf < MLE < inf, data units, closer to zero is better.
**Notes** Same as the mean erro (ME) only use log ratios as the error term. Limits the impact of outliers, more
evenly weights high and low data values.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean log error value.
Examples
--------
Note that the value is very small because it is in log space.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.mle(sim, obs)
0.002961767058151136
References
----------
- Törnqvist, Leo, Pentti Vartia, and Yrjö O. Vartia. “How Should Relative Changes Be Measured?”
The American Statistician 39, no. 1 (1985): 43–46.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
sim_log = np.log1p(simulated_array)
obs_log = np.log1p(observed_array)
return np.mean(sim_log - obs_log) | python | def mle(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the mean log error of the simulated and observed data.
.. image:: /pictures/MLE.png
**Range:** -inf < MLE < inf, data units, closer to zero is better.
**Notes** Same as the mean erro (ME) only use log ratios as the error term. Limits the impact of outliers, more
evenly weights high and low data values.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean log error value.
Examples
--------
Note that the value is very small because it is in log space.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.mle(sim, obs)
0.002961767058151136
References
----------
- Törnqvist, Leo, Pentti Vartia, and Yrjö O. Vartia. “How Should Relative Changes Be Measured?”
The American Statistician 39, no. 1 (1985): 43–46.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
sim_log = np.log1p(simulated_array)
obs_log = np.log1p(observed_array)
return np.mean(sim_log - obs_log) | Compute the mean log error of the simulated and observed data.
.. image:: /pictures/MLE.png
**Range:** -inf < MLE < inf, data units, closer to zero is better.
**Notes** Same as the mean erro (ME) only use log ratios as the error term. Limits the impact of outliers, more
evenly weights high and low data values.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean log error value.
Examples
--------
Note that the value is very small because it is in log space.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.mle(sim, obs)
0.002961767058151136
References
----------
- Törnqvist, Leo, Pentti Vartia, and Yrjö O. Vartia. “How Should Relative Changes Be Measured?”
The American Statistician 39, no. 1 (1985): 43–46. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L271-L348 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | mde | def mde(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the median error (MdE) between the simulated and observed data.
.. image:: /pictures/MdE.png
**Range** -inf < MdE < inf, closer to zero is better.
**Notes** This metric indicates bias. It is similar to the mean error (ME), only it takes the
median rather than the mean. Median measures reduces the impact of outliers.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
Note that the last outlier residual in the time series is negated using the median.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 100])
>>> he.mde(sim, obs)
-0.10000000000000009
Returns
-------
float
The median error value.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
return np.median(simulated_array - observed_array) | python | def mde(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the median error (MdE) between the simulated and observed data.
.. image:: /pictures/MdE.png
**Range** -inf < MdE < inf, closer to zero is better.
**Notes** This metric indicates bias. It is similar to the mean error (ME), only it takes the
median rather than the mean. Median measures reduces the impact of outliers.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
Note that the last outlier residual in the time series is negated using the median.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 100])
>>> he.mde(sim, obs)
-0.10000000000000009
Returns
-------
float
The median error value.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
return np.median(simulated_array - observed_array) | Compute the median error (MdE) between the simulated and observed data.
.. image:: /pictures/MdE.png
**Range** -inf < MdE < inf, closer to zero is better.
**Notes** This metric indicates bias. It is similar to the mean error (ME), only it takes the
median rather than the mean. Median measures reduces the impact of outliers.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
Note that the last outlier residual in the time series is negated using the median.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 100])
>>> he.mde(sim, obs)
-0.10000000000000009
Returns
-------
float
The median error value. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L511-L582 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | mdae | def mdae(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the median absolute error (MdAE) between the simulated and observed data.
.. image:: /pictures/MdAE.png
**Range** 0 ≤ MdAE < inf, closer to zero is better.
**Notes** Random errors (noise) do not cancel. It is the same as the mean absolute error (MAE), only it takes the
median rather than the mean. Median measures reduces the impact of outliers.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
Note that the last outlier residual in the time series is negated using the median.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 100])
>>> he.mdae(sim, obs)
0.75
Returns
-------
float
The median absolute error value.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
return np.median(np.abs(simulated_array - observed_array)) | python | def mdae(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the median absolute error (MdAE) between the simulated and observed data.
.. image:: /pictures/MdAE.png
**Range** 0 ≤ MdAE < inf, closer to zero is better.
**Notes** Random errors (noise) do not cancel. It is the same as the mean absolute error (MAE), only it takes the
median rather than the mean. Median measures reduces the impact of outliers.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
Note that the last outlier residual in the time series is negated using the median.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 100])
>>> he.mdae(sim, obs)
0.75
Returns
-------
float
The median absolute error value.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
return np.median(np.abs(simulated_array - observed_array)) | Compute the median absolute error (MdAE) between the simulated and observed data.
.. image:: /pictures/MdAE.png
**Range** 0 ≤ MdAE < inf, closer to zero is better.
**Notes** Random errors (noise) do not cancel. It is the same as the mean absolute error (MAE), only it takes the
median rather than the mean. Median measures reduces the impact of outliers.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
Note that the last outlier residual in the time series is negated using the median.
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 100])
>>> he.mdae(sim, obs)
0.75
Returns
-------
float
The median absolute error value. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L585-L656 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | ed | def ed(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the Euclidean distance between predicted and observed values in vector space.
.. image:: /pictures/ED.png
**Range** 0 ≤ ED < inf, smaller is better.
**Notes** Also sometimes referred to as the L2-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.ed(sim, obs)
1.63707055437449
Returns
-------
float
The euclidean distance error value.
References
----------
- Kennard, M. J., Mackay, S. J., Pusey, B. J., Olden, J. D., & Marsh, N. (2010). Quantifying
uncertainty in estimation of hydrologic metrics for ecohydrological studies. River Research
and Applications, 26(2), 137-156.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
return np.linalg.norm(observed_array - simulated_array) | python | def ed(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the Euclidean distance between predicted and observed values in vector space.
.. image:: /pictures/ED.png
**Range** 0 ≤ ED < inf, smaller is better.
**Notes** Also sometimes referred to as the L2-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.ed(sim, obs)
1.63707055437449
Returns
-------
float
The euclidean distance error value.
References
----------
- Kennard, M. J., Mackay, S. J., Pusey, B. J., Olden, J. D., & Marsh, N. (2010). Quantifying
uncertainty in estimation of hydrologic metrics for ecohydrological studies. River Research
and Applications, 26(2), 137-156.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
return np.linalg.norm(observed_array - simulated_array) | Compute the Euclidean distance between predicted and observed values in vector space.
.. image:: /pictures/ED.png
**Range** 0 ≤ ED < inf, smaller is better.
**Notes** Also sometimes referred to as the L2-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.ed(sim, obs)
1.63707055437449
Returns
-------
float
The euclidean distance error value.
References
----------
- Kennard, M. J., Mackay, S. J., Pusey, B. J., Olden, J. D., & Marsh, N. (2010). Quantifying
uncertainty in estimation of hydrologic metrics for ecohydrological studies. River Research
and Applications, 26(2), 137-156. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L733-L805 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | ned | def ned(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the normalized Euclidian distance between the simulated and observed data in vector
space.
.. image:: /pictures/NED.png
**Range** 0 ≤ NED < inf, smaller is better.
**Notes** Also sometimes referred to as the squared L2-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The normalized euclidean distance value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.ned(sim, obs)
0.2872053604165771
References
----------
- Kennard, M. J., Mackay, S. J., Pusey, B. J., Olden, J. D., & Marsh, N. (2010). Quantifying
uncertainty in estimation of hydrologic metrics for ecohydrological studies. River Research
and Applications, 26(2), 137-156.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = observed_array / np.mean(observed_array)
b = simulated_array / np.mean(simulated_array)
return np.linalg.norm(a - b) | python | def ned(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""
Compute the normalized Euclidian distance between the simulated and observed data in vector
space.
.. image:: /pictures/NED.png
**Range** 0 ≤ NED < inf, smaller is better.
**Notes** Also sometimes referred to as the squared L2-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The normalized euclidean distance value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.ned(sim, obs)
0.2872053604165771
References
----------
- Kennard, M. J., Mackay, S. J., Pusey, B. J., Olden, J. D., & Marsh, N. (2010). Quantifying
uncertainty in estimation of hydrologic metrics for ecohydrological studies. River Research
and Applications, 26(2), 137-156.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = observed_array / np.mean(observed_array)
b = simulated_array / np.mean(simulated_array)
return np.linalg.norm(a - b) | Compute the normalized Euclidian distance between the simulated and observed data in vector
space.
.. image:: /pictures/NED.png
**Range** 0 ≤ NED < inf, smaller is better.
**Notes** Also sometimes referred to as the squared L2-norm.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The normalized euclidean distance value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.ned(sim, obs)
0.2872053604165771
References
----------
- Kennard, M. J., Mackay, S. J., Pusey, B. J., Olden, J. D., & Marsh, N. (2010). Quantifying
uncertainty in estimation of hydrologic metrics for ecohydrological studies. River Research
and Applications, 26(2), 137-156. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L808-L883 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | nrmse_range | def nrmse_range(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the range normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_Range.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the range of the observed time series (x).
Normalizing allows comparison between data sets with different scales. The NRMSErange is the
most sensitive to outliers of the three normalized rmse metrics.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The range normalized root mean square error value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_range(sim, obs)
0.0891108340256152
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
rmse_value = np.sqrt(np.mean((simulated_array - observed_array) ** 2))
obs_max = np.max(observed_array)
obs_min = np.min(observed_array)
return rmse_value / (obs_max - obs_min) | python | def nrmse_range(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the range normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_Range.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the range of the observed time series (x).
Normalizing allows comparison between data sets with different scales. The NRMSErange is the
most sensitive to outliers of the three normalized rmse metrics.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The range normalized root mean square error value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_range(sim, obs)
0.0891108340256152
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
rmse_value = np.sqrt(np.mean((simulated_array - observed_array) ** 2))
obs_max = np.max(observed_array)
obs_min = np.min(observed_array)
return rmse_value / (obs_max - obs_min) | Compute the range normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_Range.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the range of the observed time series (x).
Normalizing allows comparison between data sets with different scales. The NRMSErange is the
most sensitive to outliers of the three normalized rmse metrics.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The range normalized root mean square error value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_range(sim, obs)
0.0891108340256152
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L1044-L1121 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | nrmse_mean | def nrmse_mean(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the mean normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_Mean.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the mean of the observed time series (x).
Normalizing allows comparison between data sets with different scales.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean normalized root mean square error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_mean(sim, obs)
0.11725109740212526
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
rmse_value = np.sqrt(np.mean((simulated_array - observed_array) ** 2))
obs_mean = np.mean(observed_array)
return rmse_value / obs_mean | python | def nrmse_mean(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the mean normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_Mean.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the mean of the observed time series (x).
Normalizing allows comparison between data sets with different scales.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean normalized root mean square error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_mean(sim, obs)
0.11725109740212526
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
rmse_value = np.sqrt(np.mean((simulated_array - observed_array) ** 2))
obs_mean = np.mean(observed_array)
return rmse_value / obs_mean | Compute the mean normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_Mean.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the mean of the observed time series (x).
Normalizing allows comparison between data sets with different scales.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean normalized root mean square error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_mean(sim, obs)
0.11725109740212526
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L1124-L1200 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | nrmse_iqr | def nrmse_iqr(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the IQR normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_IQR.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the interquartile range of the observed time
series (x). Normalizing allows comparison between data sets with different scales.
The NRMSEquartile is the least sensitive to outliers of the three normalized rmse metrics.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The IQR normalized root mean square error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_iqr(sim, obs)
0.2595461185212093
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
rmse_value = np.sqrt(np.mean((simulated_array - observed_array) ** 2))
q1 = np.percentile(observed_array, 25)
q3 = np.percentile(observed_array, 75)
iqr = q3 - q1
return rmse_value / iqr | python | def nrmse_iqr(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the IQR normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_IQR.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the interquartile range of the observed time
series (x). Normalizing allows comparison between data sets with different scales.
The NRMSEquartile is the least sensitive to outliers of the three normalized rmse metrics.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The IQR normalized root mean square error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_iqr(sim, obs)
0.2595461185212093
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
rmse_value = np.sqrt(np.mean((simulated_array - observed_array) ** 2))
q1 = np.percentile(observed_array, 25)
q3 = np.percentile(observed_array, 75)
iqr = q3 - q1
return rmse_value / iqr | Compute the IQR normalized root mean square error between the simulated and observed data.
.. image:: /pictures/NRMSE_IQR.png
**Range:** 0 ≤ NRMSE < inf.
**Notes:** This metric is the RMSE normalized by the interquartile range of the observed time
series (x). Normalizing allows comparison between data sets with different scales.
The NRMSEquartile is the least sensitive to outliers of the three normalized rmse metrics.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The IQR normalized root mean square error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_iqr(sim, obs)
0.2595461185212093
References
----------
- Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple
resolution comparison between maps that share a real variable. Environmental and Ecological
Statistics 15(2) 111-142. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L1203-L1282 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | mase | def mase(simulated_array, observed_array, m=1, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the mean absolute scaled error between the simulated and observed data.
.. image:: /pictures/MASE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
m: int
If given, indicates the seasonal period m. If not given, the default is 1.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute scaled error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.mase(sim, obs)
0.17341040462427745
References
----------
- Hyndman, R.J., Koehler, A.B., 2006. Another look at measures of forecast accuracy.
International Journal of Forecasting 22(4) 679-688.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
start = m
end = simulated_array.size - m
a = np.mean(np.abs(simulated_array - observed_array))
b = np.abs(observed_array[start:observed_array.size] - observed_array[:end])
return a / (np.sum(b) / end) | python | def mase(simulated_array, observed_array, m=1, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the mean absolute scaled error between the simulated and observed data.
.. image:: /pictures/MASE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
m: int
If given, indicates the seasonal period m. If not given, the default is 1.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute scaled error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.mase(sim, obs)
0.17341040462427745
References
----------
- Hyndman, R.J., Koehler, A.B., 2006. Another look at measures of forecast accuracy.
International Journal of Forecasting 22(4) 679-688.
"""
# Checking and cleaning the data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
start = m
end = simulated_array.size - m
a = np.mean(np.abs(simulated_array - observed_array))
b = np.abs(observed_array[start:observed_array.size] - observed_array[:end])
return a / (np.sum(b) / end) | Compute the mean absolute scaled error between the simulated and observed data.
.. image:: /pictures/MASE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
m: int
If given, indicates the seasonal period m. If not given, the default is 1.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute scaled error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.mase(sim, obs)
0.17341040462427745
References
----------
- Hyndman, R.J., Koehler, A.B., 2006. Another look at measures of forecast accuracy.
International Journal of Forecasting 22(4) 679-688. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L1372-L1450 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | maape | def maape(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the the Mean Arctangent Absolute Percentage Error (MAAPE).
.. image:: /pictures/MAAPE.png
**Range:** 0 ≤ MAAPE < π/2, does not indicate bias, smaller is better.
**Notes:** Represents the mean absolute error as a percentage of the observed values. Handles
0s in the observed data. This metric is not as biased as MAPE by under-over predictions.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean arctangent absolute percentage error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.mape(sim, obs)
11.639226612630866
References
----------
- Kim, S., Kim, H., 2016. A new metric of absolute percentage error for intermittent demand
forecasts. International Journal of Forecasting 32(3) 669-679.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = simulated_array - observed_array
b = np.abs(a / observed_array)
return np.mean(np.arctan(b)) | python | def maape(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the the Mean Arctangent Absolute Percentage Error (MAAPE).
.. image:: /pictures/MAAPE.png
**Range:** 0 ≤ MAAPE < π/2, does not indicate bias, smaller is better.
**Notes:** Represents the mean absolute error as a percentage of the observed values. Handles
0s in the observed data. This metric is not as biased as MAPE by under-over predictions.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean arctangent absolute percentage error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.mape(sim, obs)
11.639226612630866
References
----------
- Kim, S., Kim, H., 2016. A new metric of absolute percentage error for intermittent demand
forecasts. International Journal of Forecasting 32(3) 669-679.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = simulated_array - observed_array
b = np.abs(a / observed_array)
return np.mean(np.arctan(b)) | Compute the the Mean Arctangent Absolute Percentage Error (MAAPE).
.. image:: /pictures/MAAPE.png
**Range:** 0 ≤ MAAPE < π/2, does not indicate bias, smaller is better.
**Notes:** Represents the mean absolute error as a percentage of the observed values. Handles
0s in the observed data. This metric is not as biased as MAPE by under-over predictions.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean arctangent absolute percentage error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.mape(sim, obs)
11.639226612630866
References
----------
- Kim, S., Kim, H., 2016. A new metric of absolute percentage error for intermittent demand
forecasts. International Journal of Forecasting 32(3) 669-679. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L1937-L2010 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | drel | def drel(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the the relative index of agreement (drel).
.. image:: /pictures/drel.png
**Range:** 0 ≤ drel < 1, does not indicate bias, larger is better.
**Notes:** Instead of absolute differences, this metric uses relative differences.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The relative index of agreement.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.drel(sim, obs)
0.9740868625579597
References
----------
- Krause, P., Boyle, D., Bäse, F., 2005. Comparison of different efficiency criteria for
hydrological model assessment. Advances in geosciences 5 89-97.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = ((simulated_array - observed_array) / observed_array) ** 2
b = np.abs(simulated_array - np.mean(observed_array))
c = np.abs(observed_array - np.mean(observed_array))
e = ((b + c) / np.mean(observed_array)) ** 2
return 1 - (np.sum(a) / np.sum(e)) | python | def drel(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the the relative index of agreement (drel).
.. image:: /pictures/drel.png
**Range:** 0 ≤ drel < 1, does not indicate bias, larger is better.
**Notes:** Instead of absolute differences, this metric uses relative differences.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The relative index of agreement.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.drel(sim, obs)
0.9740868625579597
References
----------
- Krause, P., Boyle, D., Bäse, F., 2005. Comparison of different efficiency criteria for
hydrological model assessment. Advances in geosciences 5 89-97.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = ((simulated_array - observed_array) / observed_array) ** 2
b = np.abs(simulated_array - np.mean(observed_array))
c = np.abs(observed_array - np.mean(observed_array))
e = ((b + c) / np.mean(observed_array)) ** 2
return 1 - (np.sum(a) / np.sum(e)) | Compute the the relative index of agreement (drel).
.. image:: /pictures/drel.png
**Range:** 0 ≤ drel < 1, does not indicate bias, larger is better.
**Notes:** Instead of absolute differences, this metric uses relative differences.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The relative index of agreement.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.drel(sim, obs)
0.9740868625579597
References
----------
- Krause, P., Boyle, D., Bäse, F., 2005. Comparison of different efficiency criteria for
hydrological model assessment. Advances in geosciences 5 89-97. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L2425-L2499 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | watt_m | def watt_m(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute Watterson's M (M).
.. image:: /pictures/M.png
**Range:** -1 ≤ M < 1, does not indicate bias, larger is better.
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
Watterson's M value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.watt_m(sim, obs)
0.8307913876595929
References
----------
- Watterson, I.G., 1996. Non‐dimensional measures of climate model performance. International
Journal of Climatology 16(4) 379-391.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = 2 / np.pi
b = np.mean((simulated_array - observed_array) ** 2) # MSE
c = np.std(observed_array, ddof=1) ** 2 + np.std(simulated_array, ddof=1) ** 2
e = (np.mean(simulated_array) - np.mean(observed_array)) ** 2
f = c + e
return a * np.arcsin(1 - (b / f)) | python | def watt_m(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute Watterson's M (M).
.. image:: /pictures/M.png
**Range:** -1 ≤ M < 1, does not indicate bias, larger is better.
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
Watterson's M value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.watt_m(sim, obs)
0.8307913876595929
References
----------
- Watterson, I.G., 1996. Non‐dimensional measures of climate model performance. International
Journal of Climatology 16(4) 379-391.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = 2 / np.pi
b = np.mean((simulated_array - observed_array) ** 2) # MSE
c = np.std(observed_array, ddof=1) ** 2 + np.std(simulated_array, ddof=1) ** 2
e = (np.mean(simulated_array) - np.mean(observed_array)) ** 2
f = c + e
return a * np.arcsin(1 - (b / f)) | Compute Watterson's M (M).
.. image:: /pictures/M.png
**Range:** -1 ≤ M < 1, does not indicate bias, larger is better.
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
Watterson's M value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.watt_m(sim, obs)
0.8307913876595929
References
----------
- Watterson, I.G., 1996. Non‐dimensional measures of climate model performance. International
Journal of Climatology 16(4) 379-391. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L2593-L2668 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | kge_2009 | def kge_2009(simulated_array, observed_array, s=(1, 1, 1), replace_nan=None,
replace_inf=None, remove_neg=False, remove_zero=False, return_all=False):
"""Compute the Kling-Gupta efficiency (2009).
.. image:: /pictures/KGE_2009.png
**Range:** -inf < KGE (2009) < 1, larger is better.
**Notes:** Gupta et al. (2009) created this metric to demonstrate the relative importance of
the three components of the NSE, which are correlation, bias and variability. This was done
with hydrologic modeling as the context. This metric is meant to address issues with the NSE.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
s: tuple of length three
Represents the scaling factors to be used for re-scaling the Pearson product-moment
correlation coefficient (r), Alpha, and Beta, respectively.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
return_all: bool
If True, returns all of the components of the KGE metric, which are r, alpha, and beta, respectively.
Returns
-------
float (tuple of float)
The Kling-Gupta (2009) efficiency value, unless the return_all parameter is True.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.kge_2009(sim, obs)
0.912223072345668
>>> he.kge_2009(sim, obs, return_all=True) # Returns (r, alpha, beta, kge)
(0.9615951377405804, 0.927910707932087, 1.0058823529411764, 0.9181073779138655)
References
----------
- Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean
squared error and NSE performance criteria: Implications for improving hydrological modelling.
Journal of Hydrology, 377(1-2), 80-91.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
# Means
sim_mean = np.mean(simulated_array)
obs_mean = np.mean(observed_array)
# Standard Deviations
sim_sigma = np.std(simulated_array, ddof=1)
obs_sigma = np.std(observed_array, ddof=1)
# Pearson R
top_pr = np.sum((observed_array - obs_mean) * (simulated_array - sim_mean))
bot1_pr = np.sqrt(np.sum((observed_array - obs_mean) ** 2))
bot2_pr = np.sqrt(np.sum((simulated_array - sim_mean) ** 2))
pr = top_pr / (bot1_pr * bot2_pr)
# Ratio between mean of simulated and observed data
if obs_mean != 0:
beta = sim_mean / obs_mean
else:
beta = np.nan
# Relative variability between simulated and observed values
if obs_sigma != 0:
alpha = sim_sigma / obs_sigma
else:
alpha = np.nan
if not np.isnan(beta) and not np.isnan(alpha):
kge = 1 - np.sqrt(
(s[0] * (pr - 1)) ** 2 + (s[1] * (alpha - 1)) ** 2 + (s[2] * (beta - 1)) ** 2)
else:
if obs_mean == 0:
warnings.warn(
'Warning: The observed data mean is 0. Therefore, Beta is infinite and the KGE '
'value cannot be computed.')
if obs_sigma == 0:
warnings.warn(
'Warning: The observed data standard deviation is 0. Therefore, Alpha is infinite '
'and the KGE value cannot be computed.')
kge = np.nan
assert type(return_all) == bool, "expected <type 'bool'> for parameter return_all, got {}".format(type(return_all))
if return_all:
return pr, alpha, beta, kge
else:
return kge | python | def kge_2009(simulated_array, observed_array, s=(1, 1, 1), replace_nan=None,
replace_inf=None, remove_neg=False, remove_zero=False, return_all=False):
"""Compute the Kling-Gupta efficiency (2009).
.. image:: /pictures/KGE_2009.png
**Range:** -inf < KGE (2009) < 1, larger is better.
**Notes:** Gupta et al. (2009) created this metric to demonstrate the relative importance of
the three components of the NSE, which are correlation, bias and variability. This was done
with hydrologic modeling as the context. This metric is meant to address issues with the NSE.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
s: tuple of length three
Represents the scaling factors to be used for re-scaling the Pearson product-moment
correlation coefficient (r), Alpha, and Beta, respectively.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
return_all: bool
If True, returns all of the components of the KGE metric, which are r, alpha, and beta, respectively.
Returns
-------
float (tuple of float)
The Kling-Gupta (2009) efficiency value, unless the return_all parameter is True.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.kge_2009(sim, obs)
0.912223072345668
>>> he.kge_2009(sim, obs, return_all=True) # Returns (r, alpha, beta, kge)
(0.9615951377405804, 0.927910707932087, 1.0058823529411764, 0.9181073779138655)
References
----------
- Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean
squared error and NSE performance criteria: Implications for improving hydrological modelling.
Journal of Hydrology, 377(1-2), 80-91.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
# Means
sim_mean = np.mean(simulated_array)
obs_mean = np.mean(observed_array)
# Standard Deviations
sim_sigma = np.std(simulated_array, ddof=1)
obs_sigma = np.std(observed_array, ddof=1)
# Pearson R
top_pr = np.sum((observed_array - obs_mean) * (simulated_array - sim_mean))
bot1_pr = np.sqrt(np.sum((observed_array - obs_mean) ** 2))
bot2_pr = np.sqrt(np.sum((simulated_array - sim_mean) ** 2))
pr = top_pr / (bot1_pr * bot2_pr)
# Ratio between mean of simulated and observed data
if obs_mean != 0:
beta = sim_mean / obs_mean
else:
beta = np.nan
# Relative variability between simulated and observed values
if obs_sigma != 0:
alpha = sim_sigma / obs_sigma
else:
alpha = np.nan
if not np.isnan(beta) and not np.isnan(alpha):
kge = 1 - np.sqrt(
(s[0] * (pr - 1)) ** 2 + (s[1] * (alpha - 1)) ** 2 + (s[2] * (beta - 1)) ** 2)
else:
if obs_mean == 0:
warnings.warn(
'Warning: The observed data mean is 0. Therefore, Beta is infinite and the KGE '
'value cannot be computed.')
if obs_sigma == 0:
warnings.warn(
'Warning: The observed data standard deviation is 0. Therefore, Alpha is infinite '
'and the KGE value cannot be computed.')
kge = np.nan
assert type(return_all) == bool, "expected <type 'bool'> for parameter return_all, got {}".format(type(return_all))
if return_all:
return pr, alpha, beta, kge
else:
return kge | Compute the Kling-Gupta efficiency (2009).
.. image:: /pictures/KGE_2009.png
**Range:** -inf < KGE (2009) < 1, larger is better.
**Notes:** Gupta et al. (2009) created this metric to demonstrate the relative importance of
the three components of the NSE, which are correlation, bias and variability. This was done
with hydrologic modeling as the context. This metric is meant to address issues with the NSE.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
s: tuple of length three
Represents the scaling factors to be used for re-scaling the Pearson product-moment
correlation coefficient (r), Alpha, and Beta, respectively.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
return_all: bool
If True, returns all of the components of the KGE metric, which are r, alpha, and beta, respectively.
Returns
-------
float (tuple of float)
The Kling-Gupta (2009) efficiency value, unless the return_all parameter is True.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.kge_2009(sim, obs)
0.912223072345668
>>> he.kge_2009(sim, obs, return_all=True) # Returns (r, alpha, beta, kge)
(0.9615951377405804, 0.927910707932087, 1.0058823529411764, 0.9181073779138655)
References
----------
- Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean
squared error and NSE performance criteria: Implications for improving hydrological modelling.
Journal of Hydrology, 377(1-2), 80-91. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L3023-L3151 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | sa | def sa(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the Spectral Angle (SA).
.. image:: /pictures/SA.png
**Range:** -π/2 ≤ SA < π/2, closer to 0 is better.
**Notes:** The spectral angle metric measures the angle between the two vectors in hyperspace.
It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Angle value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sa(sim, obs)
0.10816831366492945
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = np.dot(simulated_array, observed_array)
b = np.linalg.norm(simulated_array) * np.linalg.norm(observed_array)
return np.arccos(a / b) | python | def sa(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the Spectral Angle (SA).
.. image:: /pictures/SA.png
**Range:** -π/2 ≤ SA < π/2, closer to 0 is better.
**Notes:** The spectral angle metric measures the angle between the two vectors in hyperspace.
It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Angle value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sa(sim, obs)
0.10816831366492945
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = np.dot(simulated_array, observed_array)
b = np.linalg.norm(simulated_array) * np.linalg.norm(observed_array)
return np.arccos(a / b) | Compute the Spectral Angle (SA).
.. image:: /pictures/SA.png
**Range:** -π/2 ≤ SA < π/2, closer to 0 is better.
**Notes:** The spectral angle metric measures the angle between the two vectors in hyperspace.
It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Angle value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sa(sim, obs)
0.10816831366492945
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L3538-L3612 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | sc | def sc(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the Spectral Correlation (SC).
.. image:: /pictures/SC.png
**Range:** -π/2 ≤ SA < π/2, closer to 0 is better.
**Notes:** The spectral correlation metric measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Correlation value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sc(sim, obs)
0.27991341383646606
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = np.dot(observed_array - np.mean(observed_array), simulated_array - np.mean(simulated_array))
b = np.linalg.norm(observed_array - np.mean(observed_array))
c = np.linalg.norm(simulated_array - np.mean(simulated_array))
e = b * c
return np.arccos(a / e) | python | def sc(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the Spectral Correlation (SC).
.. image:: /pictures/SC.png
**Range:** -π/2 ≤ SA < π/2, closer to 0 is better.
**Notes:** The spectral correlation metric measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Correlation value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sc(sim, obs)
0.27991341383646606
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
a = np.dot(observed_array - np.mean(observed_array), simulated_array - np.mean(simulated_array))
b = np.linalg.norm(observed_array - np.mean(observed_array))
c = np.linalg.norm(simulated_array - np.mean(simulated_array))
e = b * c
return np.arccos(a / e) | Compute the Spectral Correlation (SC).
.. image:: /pictures/SC.png
**Range:** -π/2 ≤ SA < π/2, closer to 0 is better.
**Notes:** The spectral correlation metric measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Correlation value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sc(sim, obs)
0.27991341383646606
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L3615-L3691 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | sid | def sid(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the Spectral Information Divergence (SID).
.. image:: /pictures/SID.png
**Range:** -π/2 ≤ SID < π/2, closer to 0 is better.
**Notes:** The spectral information divergence measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral information divergence value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sid(sim, obs)
0.03517616895318012
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
first = (observed_array / np.mean(observed_array)) - (
simulated_array / np.mean(simulated_array))
second1 = np.log10(observed_array) - np.log10(np.mean(observed_array))
second2 = np.log10(simulated_array) - np.log10(np.mean(simulated_array))
return np.dot(first, second1 - second2) | python | def sid(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the Spectral Information Divergence (SID).
.. image:: /pictures/SID.png
**Range:** -π/2 ≤ SID < π/2, closer to 0 is better.
**Notes:** The spectral information divergence measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral information divergence value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sid(sim, obs)
0.03517616895318012
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
first = (observed_array / np.mean(observed_array)) - (
simulated_array / np.mean(simulated_array))
second1 = np.log10(observed_array) - np.log10(np.mean(observed_array))
second2 = np.log10(simulated_array) - np.log10(np.mean(simulated_array))
return np.dot(first, second1 - second2) | Compute the Spectral Information Divergence (SID).
.. image:: /pictures/SID.png
**Range:** -π/2 ≤ SID < π/2, closer to 0 is better.
**Notes:** The spectral information divergence measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral information divergence value.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sid(sim, obs)
0.03517616895318012
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L3694-L3770 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | sga | def sga(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the Spectral Gradient Angle (SGA).
.. image:: /pictures/SGA.png
**Range:** -π/2 ≤ SID < π/2, closer to 0 is better.
**Notes:** The spectral gradient angle measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
SG is the gradient of the simulated or observed time series.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Gradient Angle.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sga(sim, obs)
0.26764286472739834
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
sgx = observed_array[1:] - observed_array[:observed_array.size - 1]
sgy = simulated_array[1:] - simulated_array[:simulated_array.size - 1]
a = np.dot(sgx, sgy)
b = np.linalg.norm(sgx) * np.linalg.norm(sgy)
return np.arccos(a / b) | python | def sga(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the Spectral Gradient Angle (SGA).
.. image:: /pictures/SGA.png
**Range:** -π/2 ≤ SID < π/2, closer to 0 is better.
**Notes:** The spectral gradient angle measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
SG is the gradient of the simulated or observed time series.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Gradient Angle.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sga(sim, obs)
0.26764286472739834
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
sgx = observed_array[1:] - observed_array[:observed_array.size - 1]
sgy = simulated_array[1:] - simulated_array[:simulated_array.size - 1]
a = np.dot(sgx, sgy)
b = np.linalg.norm(sgx) * np.linalg.norm(sgy)
return np.arccos(a / b) | Compute the Spectral Gradient Angle (SGA).
.. image:: /pictures/SGA.png
**Range:** -π/2 ≤ SID < π/2, closer to 0 is better.
**Notes:** The spectral gradient angle measures the angle between the two vectors in
hyperspace. It indicates how well the shape of the two series match – not magnitude.
SG is the gradient of the simulated or observed time series.
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The Spectral Gradient Angle.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sga(sim, obs)
0.26764286472739834
References
----------
- Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
pp. 163-166. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L3773-L3850 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | h1_mhe | def h1_mhe(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the H1 mean error.
.. image:: /pictures/H1.png
.. image:: /pictures/MHE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean H1 error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.h1_mhe(sim, obs)
0.002106551840594386
References
----------
- Tornquist, L., Vartia, P., Vartia, Y.O., 1985. How Should Relative Changes be Measured?
The American Statistician 43-46.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
h = (simulated_array - observed_array) / observed_array
return np.mean(h) | python | def h1_mhe(simulated_array, observed_array, replace_nan=None, replace_inf=None,
remove_neg=False, remove_zero=False):
"""Compute the H1 mean error.
.. image:: /pictures/H1.png
.. image:: /pictures/MHE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean H1 error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.h1_mhe(sim, obs)
0.002106551840594386
References
----------
- Tornquist, L., Vartia, P., Vartia, Y.O., 1985. How Should Relative Changes be Measured?
The American Statistician 43-46.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
h = (simulated_array - observed_array) / observed_array
return np.mean(h) | Compute the H1 mean error.
.. image:: /pictures/H1.png
.. image:: /pictures/MHE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean H1 error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.h1_mhe(sim, obs)
0.002106551840594386
References
----------
- Tornquist, L., Vartia, P., Vartia, Y.O., 1985. How Should Relative Changes be Measured?
The American Statistician 43-46. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L3858-L3930 |
BYU-Hydroinformatics/HydroErr | HydroErr/HydroErr.py | h6_mahe | def h6_mahe(simulated_array, observed_array, k=1, replace_nan=None, replace_inf=None,
remove_neg=False,
remove_zero=False):
"""Compute the H6 mean absolute error.
.. image:: /pictures/H6.png
.. image:: /pictures/AHE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
k: int or float
If given, sets the value of k. If None, k=1.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute H6 error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.h6_mahe(sim, obs)
0.11743831388794852
References
----------
- Tornquist, L., Vartia, P., Vartia, Y.O., 1985. How Should Relative Changes be Measured?
The American Statistician 43-46.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
top = (simulated_array / observed_array - 1)
bot = np.power(0.5 * (1 + np.power(simulated_array / observed_array, k)), 1 / k)
h = top / bot
return np.mean(np.abs(h)) | python | def h6_mahe(simulated_array, observed_array, k=1, replace_nan=None, replace_inf=None,
remove_neg=False,
remove_zero=False):
"""Compute the H6 mean absolute error.
.. image:: /pictures/H6.png
.. image:: /pictures/AHE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
k: int or float
If given, sets the value of k. If None, k=1.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute H6 error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.h6_mahe(sim, obs)
0.11743831388794852
References
----------
- Tornquist, L., Vartia, P., Vartia, Y.O., 1985. How Should Relative Changes be Measured?
The American Statistician 43-46.
"""
# Treats data
simulated_array, observed_array = treat_values(
simulated_array,
observed_array,
replace_nan=replace_nan,
replace_inf=replace_inf,
remove_neg=remove_neg,
remove_zero=remove_zero
)
top = (simulated_array / observed_array - 1)
bot = np.power(0.5 * (1 + np.power(simulated_array / observed_array, k)), 1 / k)
h = top / bot
return np.mean(np.abs(h)) | Compute the H6 mean absolute error.
.. image:: /pictures/H6.png
.. image:: /pictures/AHE.png
**Range:**
**Notes:**
Parameters
----------
simulated_array: one dimensional ndarray
An array of simulated data from the time series.
observed_array: one dimensional ndarray
An array of observed data from the time series.
k: int or float
If given, sets the value of k. If None, k=1.
replace_nan: float, optional
If given, indicates which value to replace NaN values with in the two arrays. If None, when
a NaN value is found at the i-th position in the observed OR simulated array, the i-th value
of the observed and simulated array are removed before the computation.
replace_inf: float, optional
If given, indicates which value to replace Inf values with in the two arrays. If None, when
an inf value is found at the i-th position in the observed OR simulated array, the i-th
value of the observed and simulated array are removed before the computation.
remove_neg: boolean, optional
If True, when a negative value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
remove_zero: boolean, optional
If true, when a zero value is found at the i-th position in the observed OR simulated
array, the i-th value of the observed AND simulated array are removed before the
computation.
Returns
-------
float
The mean absolute H6 error.
Examples
--------
>>> import HydroErr as he
>>> import numpy as np
>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.h6_mahe(sim, obs)
0.11743831388794852
References
----------
- Tornquist, L., Vartia, P., Vartia, Y.O., 1985. How Should Relative Changes be Measured?
The American Statistician 43-46. | https://github.com/BYU-Hydroinformatics/HydroErr/blob/42a84f3e006044f450edc7393ed54d59f27ef35b/HydroErr/HydroErr.py#L5110-L5189 |
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