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billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.connect | def connect(self):
"""|coro|
Connect to ubisoft, automatically called when needed"""
if time.time() < self._login_cooldown:
raise FailedToConnect("login on cooldown")
resp = yield from self.session.post("https://connect.ubi.com/ubiservices/v2/profiles/sessions", headers = {
"Content-Type": "application/json",
"Ubi-AppId": self.appid,
"Authorization": "Basic " + self.token
}, data=json.dumps({"rememberMe": True}))
data = yield from resp.json()
if "ticket" in data:
self.key = data.get("ticket")
self.sessionid = data.get("sessionId")
self.uncertain_spaceid = data.get("spaceId")
else:
raise FailedToConnect | python | def connect(self):
if time.time() < self._login_cooldown:
raise FailedToConnect("login on cooldown")
resp = yield from self.session.post("https://connect.ubi.com/ubiservices/v2/profiles/sessions", headers = {
"Content-Type": "application/json",
"Ubi-AppId": self.appid,
"Authorization": "Basic " + self.token
}, data=json.dumps({"rememberMe": True}))
data = yield from resp.json()
if "ticket" in data:
self.key = data.get("ticket")
self.sessionid = data.get("sessionId")
self.uncertain_spaceid = data.get("spaceId")
else:
raise FailedToConnect | [
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| |coro|
Connect to ubisoft, automatically called when needed | [
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]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L378-L398 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.get_players | def get_players(self, name=None, platform=None, uid=None):
"""|coro|
get a list of players matching the term on that platform,
exactly one of uid and name must be given, platform must be given,
this list almost always has only 1 element, so it's easier to use get_player
Parameters
----------
name : str
the name of the player you're searching for
platform : str
the name of the platform you're searching on (See :class:`Platforms`)
uid : str
the uid of the player you're searching for
Returns
-------
list[:class:`Player`]
list of found players"""
if name is None and uid is None:
raise TypeError("name and uid are both None, exactly one must be given")
if name is not None and uid is not None:
raise TypeError("cannot search by uid and name at the same time, please give one or the other")
if platform is None:
raise TypeError("platform cannot be None")
if "platform" not in self.cache: self.cache[platform] = {}
if name:
cache_key = "NAME:%s" % name
else:
cache_key = "UID:%s" % uid
if cache_key in self.cache[platform]:
if self.cachetime > 0 and self.cache[platform][cache_key][0] < time.time():
del self.cache[platform][cache_key]
else:
return self.cache[platform][cache_key][1]
if name:
data = yield from self.get("https://public-ubiservices.ubi.com/v2/profiles?nameOnPlatform=%s&platformType=%s" % (parse.quote(name), parse.quote(platform)))
else:
data = yield from self.get("https://public-ubiservices.ubi.com/v2/users/%s/profiles?platformType=%s" % (uid, parse.quote(platform)))
if "profiles" in data:
results = [Player(self, x) for x in data["profiles"] if x.get("platformType", "") == platform]
if len(results) == 0: raise InvalidRequest("No results")
if self.cachetime != 0:
self.cache[platform][cache_key] = [time.time() + self.cachetime, results]
return results
else:
raise InvalidRequest("Missing key profiles in returned JSON object %s" % str(data)) | python | def get_players(self, name=None, platform=None, uid=None):
if name is None and uid is None:
raise TypeError("name and uid are both None, exactly one must be given")
if name is not None and uid is not None:
raise TypeError("cannot search by uid and name at the same time, please give one or the other")
if platform is None:
raise TypeError("platform cannot be None")
if "platform" not in self.cache: self.cache[platform] = {}
if name:
cache_key = "NAME:%s" % name
else:
cache_key = "UID:%s" % uid
if cache_key in self.cache[platform]:
if self.cachetime > 0 and self.cache[platform][cache_key][0] < time.time():
del self.cache[platform][cache_key]
else:
return self.cache[platform][cache_key][1]
if name:
data = yield from self.get("https://public-ubiservices.ubi.com/v2/profiles?nameOnPlatform=%s&platformType=%s" % (parse.quote(name), parse.quote(platform)))
else:
data = yield from self.get("https://public-ubiservices.ubi.com/v2/users/%s/profiles?platformType=%s" % (uid, parse.quote(platform)))
if "profiles" in data:
results = [Player(self, x) for x in data["profiles"] if x.get("platformType", "") == platform]
if len(results) == 0: raise InvalidRequest("No results")
if self.cachetime != 0:
self.cache[platform][cache_key] = [time.time() + self.cachetime, results]
return results
else:
raise InvalidRequest("Missing key profiles in returned JSON object %s" % str(data)) | [
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| |coro|
get a list of players matching the term on that platform,
exactly one of uid and name must be given, platform must be given,
this list almost always has only 1 element, so it's easier to use get_player
Parameters
----------
name : str
the name of the player you're searching for
platform : str
the name of the platform you're searching on (See :class:`Platforms`)
uid : str
the uid of the player you're searching for
Returns
-------
list[:class:`Player`]
list of found players | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L460-L515 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.get_player | def get_player(self, name=None, platform=None, uid=None):
"""|coro|
Calls get_players and returns the first element,
exactly one of uid and name must be given, platform must be given
Parameters
----------
name : str
the name of the player you're searching for
platform : str
the name of the platform you're searching on (See :class:`Platforms`)
uid : str
the uid of the player you're searching for
Returns
-------
:class:`Player`
player found"""
results = yield from self.get_players(name=name, platform=platform, uid=uid)
return results[0] | python | def get_player(self, name=None, platform=None, uid=None):
results = yield from self.get_players(name=name, platform=platform, uid=uid)
return results[0] | [
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Calls get_players and returns the first element,
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Parameters
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name : str
the name of the player you're searching for
platform : str
the name of the platform you're searching on (See :class:`Platforms`)
uid : str
the uid of the player you're searching for
Returns
-------
:class:`Player`
player found | [
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]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L518-L539 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.get_operator_definitions | def get_operator_definitions(self):
"""|coro|
Retrieves a list of information about operators - their badge, unique statistic, etc.
Returns
-------
dict
operators"""
if self._op_definitions is not None:
return self._op_definitions
resp = yield from self.session.get("https://game-rainbow6.ubi.com/assets/data/operators.24b865895.json")
data = yield from resp.json()
self._op_definitions = data
return data | python | def get_operator_definitions(self):
if self._op_definitions is not None:
return self._op_definitions
resp = yield from self.session.get("https://game-rainbow6.ubi.com/assets/data/operators.24b865895.json")
data = yield from resp.json()
self._op_definitions = data
return data | [
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Retrieves a list of information about operators - their badge, unique statistic, etc.
Returns
-------
dict
operators | [
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]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L542-L558 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.get_operator_index | def get_operator_index(self, name):
"""|coro|
Gets the operators index from the operator definitions dict
Returns
-------
str
the operator index"""
opdefs = yield from self.get_operator_definitions()
name = name.lower()
if name not in opdefs:
return None
return opdefs[name]["index"] | python | def get_operator_index(self, name):
opdefs = yield from self.get_operator_definitions()
name = name.lower()
if name not in opdefs:
return None
return opdefs[name]["index"] | [
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| |coro|
Gets the operators index from the operator definitions dict
Returns
-------
str
the operator index | [
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]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L561-L576 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.get_operator_statistic | def get_operator_statistic(self, name):
"""|coro|
Gets the operator unique statistic from the operator definitions dict
Returns
-------
str
the name of the operator unique statistic"""
opdefs = yield from self.get_operator_definitions()
name = name.lower()
if name not in opdefs:
return None
# some operators (e.g. Kaid and Nomad) don't have a unique statistic sectoin for some reason...
if "uniqueStatistic" not in opdefs[name] or "pvp" not in opdefs[name]["uniqueStatistic"]:
return None
return opdefs[name]["uniqueStatistic"]["pvp"]["statisticId"] | python | def get_operator_statistic(self, name):
opdefs = yield from self.get_operator_definitions()
name = name.lower()
if name not in opdefs:
return None
if "uniqueStatistic" not in opdefs[name] or "pvp" not in opdefs[name]["uniqueStatistic"]:
return None
return opdefs[name]["uniqueStatistic"]["pvp"]["statisticId"] | [
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Returns
-------
str
the name of the operator unique statistic | [
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| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L579-L598 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.get_operator_badge | def get_operator_badge(self, name):
"""|coro|
Gets the operator badge URL
Returns
-------
str
the operators badge URL"""
opdefs = yield from self.get_operator_definitions()
name = name.lower()
if name not in opdefs:
return None
badge = opdefs[name]["badge"]
if not badge.startswith("http"):
badge = "https://game-rainbow6.ubi.com/" + badge
return badge | python | def get_operator_badge(self, name):
opdefs = yield from self.get_operator_definitions()
name = name.lower()
if name not in opdefs:
return None
badge = opdefs[name]["badge"]
if not badge.startswith("http"):
badge = "https://game-rainbow6.ubi.com/" + badge
return badge | [
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| |coro|
Gets the operator badge URL
Returns
-------
str
the operators badge URL | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L601-L621 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.get_definitions | def get_definitions(self):
"""|coro|
Retrieves the list of api definitions, downloading it from Ubisoft if it hasn't been fetched all ready
Primarily for internal use, but could contain useful information.
Returns
-------
dict
definitions"""
if self._definitions is not None:
return self._definitions
resp = yield from self.session.get("https://ubistatic-a.akamaihd.net/0058/prod/assets/data/statistics.definitions.eb165e13.json")
data = yield from resp.json()
self._definitions = data
return data | python | def get_definitions(self):
if self._definitions is not None:
return self._definitions
resp = yield from self.session.get("https://ubistatic-a.akamaihd.net/0058/prod/assets/data/statistics.definitions.eb165e13.json")
data = yield from resp.json()
self._definitions = data
return data | [
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| |coro|
Retrieves the list of api definitions, downloading it from Ubisoft if it hasn't been fetched all ready
Primarily for internal use, but could contain useful information.
Returns
-------
dict
definitions | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L625-L642 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Auth.get_object_index | def get_object_index(self, key):
"""|coro|
Mainly for internal use with get_operator,
returns the "location" index for the key in the definitions
Returns
-------
str
the object's location index"""
defns = yield from self.get_definitions()
for x in defns:
if key in x and "objectIndex" in defns[x]:
return defns[x]["objectIndex"]
return None | python | def get_object_index(self, key):
defns = yield from self.get_definitions()
for x in defns:
if key in x and "objectIndex" in defns[x]:
return defns[x]["objectIndex"]
return None | [
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returns the "location" index for the key in the definitions
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| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L645-L661 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Rank.get_charm_url | def get_charm_url(self):
"""Get charm URL for the bracket this rank is in
Returns
-------
:class:`str`
the URL for the charm
"""
if self.rank_id <= 4: return self.RANK_CHARMS[0]
if self.rank_id <= 8: return self.RANK_CHARMS[1]
if self.rank_id <= 12: return self.RANK_CHARMS[2]
if self.rank_id <= 16: return self.RANK_CHARMS[3]
if self.rank_id <= 19: return self.RANK_CHARMS[4]
return self.RANK_CHARMS[5] | python | def get_charm_url(self):
if self.rank_id <= 4: return self.RANK_CHARMS[0]
if self.rank_id <= 8: return self.RANK_CHARMS[1]
if self.rank_id <= 12: return self.RANK_CHARMS[2]
if self.rank_id <= 16: return self.RANK_CHARMS[3]
if self.rank_id <= 19: return self.RANK_CHARMS[4]
return self.RANK_CHARMS[5] | [
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| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L808-L822 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_level | def load_level(self):
"""|coro|
Load the players XP and level"""
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/r6playerprofile/playerprofile/progressions?profile_ids=%s" % (self.spaceid, self.platform_url, self.id))
if "player_profiles" in data and len(data["player_profiles"]) > 0:
self.xp = data["player_profiles"][0].get("xp", 0)
self.level = data["player_profiles"][0].get("level", 0)
else:
raise InvalidRequest("Missing key player_profiles in returned JSON object %s" % str(data)) | python | def load_level(self):
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/r6playerprofile/playerprofile/progressions?profile_ids=%s" % (self.spaceid, self.platform_url, self.id))
if "player_profiles" in data and len(data["player_profiles"]) > 0:
self.xp = data["player_profiles"][0].get("xp", 0)
self.level = data["player_profiles"][0].get("level", 0)
else:
raise InvalidRequest("Missing key player_profiles in returned JSON object %s" % str(data)) | [
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Load the players XP and level | [
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| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1093-L1103 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_rank | def load_rank(self, region, season=-1):
"""|coro|
Loads the players rank for this region and season
Parameters
----------
region : str
the name of the region you want to get the rank for
season : Optional[int]
the season you want to get the rank for (defaults to -1, latest season)
Returns
-------
:class:`Rank`
the players rank for this region and season"""
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/r6karma/players?board_id=pvp_ranked&profile_ids=%s®ion_id=%s&season_id=%s" % (self.spaceid, self.platform_url, self.id, region, season))
if "players" in data and self.id in data["players"]:
regionkey = "%s:%s" % (region, season)
self.ranks[regionkey] = Rank(data["players"][self.id])
return self.ranks[regionkey]
else:
raise InvalidRequest("Missing players key in returned JSON object %s" % str(data)) | python | def load_rank(self, region, season=-1):
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/r6karma/players?board_id=pvp_ranked&profile_ids=%s®ion_id=%s&season_id=%s" % (self.spaceid, self.platform_url, self.id, region, season))
if "players" in data and self.id in data["players"]:
regionkey = "%s:%s" % (region, season)
self.ranks[regionkey] = Rank(data["players"][self.id])
return self.ranks[regionkey]
else:
raise InvalidRequest("Missing players key in returned JSON object %s" % str(data)) | [
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Loads the players rank for this region and season
Parameters
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region : str
the name of the region you want to get the rank for
season : Optional[int]
the season you want to get the rank for (defaults to -1, latest season)
Returns
-------
:class:`Rank`
the players rank for this region and season | [
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| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1114-L1136 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.get_rank | def get_rank(self, region, season=-1):
"""|coro|
Checks the players rank for this region, only loading it if it hasn't already been found
Parameters
----------
region : str
the name of the region you want to get the rank for
season : Optional[int]
the season you want to get the rank for (defaults to -1, latest season)
Returns
-------
:class:`Rank`
the players rank for this region and season"""
cache_key = "%s:%s" % (region, season)
if cache_key in self.ranks:
return self.ranks[cache_key]
result = yield from self.load_rank(region, season)
return result | python | def get_rank(self, region, season=-1):
cache_key = "%s:%s" % (region, season)
if cache_key in self.ranks:
return self.ranks[cache_key]
result = yield from self.load_rank(region, season)
return result | [
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season : Optional[int]
the season you want to get the rank for (defaults to -1, latest season)
Returns
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:class:`Rank`
the players rank for this region and season | [
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| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1139-L1160 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_all_operators | def load_all_operators(self):
"""|coro|
Loads the player stats for all operators
Returns
-------
dict[:class:`Operator`]
the dictionary of all operators found"""
statistics = "operatorpvp_kills,operatorpvp_death,operatorpvp_roundwon,operatorpvp_roundlost,operatorpvp_meleekills,operatorpvp_totalxp,operatorpvp_headshot,operatorpvp_timeplayed,operatorpvp_dbno"
for operator in OperatorStatisticNames:
operator_key = yield from self.auth.get_operator_statistic(operator)
if operator_key:
statistics += "," + operator_key
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/playerstats2/statistics?populations=%s&statistics=%s" % (self.spaceid, self.platform_url, self.id, statistics))
if "results" not in data or not self.id in data["results"]:
raise InvalidRequest("Missing results key in returned JSON object %s" % str(data))
data = data["results"][self.id]
for operator in OperatorStatisticNames:
location = yield from self.auth.get_operator_index(operator.lower())
op_data = {x.split(":")[0].split("_")[1]: data[x] for x in data if x is not None and location in x}
operator_key = yield from self.auth.get_operator_statistic(operator)
if operator_key:
op_data["__statistic_name"] = operator_key.split("_")[1]
self.operators[operator.lower()] = Operator(operator.lower(), op_data)
return self.operators | python | def load_all_operators(self):
statistics = "operatorpvp_kills,operatorpvp_death,operatorpvp_roundwon,operatorpvp_roundlost,operatorpvp_meleekills,operatorpvp_totalxp,operatorpvp_headshot,operatorpvp_timeplayed,operatorpvp_dbno"
for operator in OperatorStatisticNames:
operator_key = yield from self.auth.get_operator_statistic(operator)
if operator_key:
statistics += "," + operator_key
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/playerstats2/statistics?populations=%s&statistics=%s" % (self.spaceid, self.platform_url, self.id, statistics))
if "results" not in data or not self.id in data["results"]:
raise InvalidRequest("Missing results key in returned JSON object %s" % str(data))
data = data["results"][self.id]
for operator in OperatorStatisticNames:
location = yield from self.auth.get_operator_index(operator.lower())
op_data = {x.split(":")[0].split("_")[1]: data[x] for x in data if x is not None and location in x}
operator_key = yield from self.auth.get_operator_statistic(operator)
if operator_key:
op_data["__statistic_name"] = operator_key.split("_")[1]
self.operators[operator.lower()] = Operator(operator.lower(), op_data)
return self.operators | [
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dict[:class:`Operator`]
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| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1163-L1195 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.get_all_operators | def get_all_operators(self):
"""|coro|
Checks the player stats for all operators, loading them all again if any aren't found
This is significantly more efficient than calling get_operator for every operator name.
Returns
-------
dict[:class:`Operator`]
the dictionary of all operators found"""
if len(self.operators) >= len(OperatorStatisticNames):
return self.operators
result = yield from self.load_all_operators()
return result | python | def get_all_operators(self):
if len(self.operators) >= len(OperatorStatisticNames):
return self.operators
result = yield from self.load_all_operators()
return result | [
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| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1198-L1212 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_operator | def load_operator(self, operator):
"""|coro|
Loads the players stats for the operator
Parameters
----------
operator : str
the name of the operator
Returns
-------
:class:`Operator`
the operator object found"""
location = yield from self.auth.get_operator_index(operator)
if location is None:
raise ValueError("invalid operator %s" % operator)
operator_key = yield from self.auth.get_operator_statistic(operator)
if operator_key is not None:
operator_key = "," + operator_key
else:
operator_key = ""
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/playerstats2/statistics?populations=%s&statistics=operatorpvp_kills,operatorpvp_death,operatorpvp_roundwon,operatorpvp_roundlost,operatorpvp_meleekills,operatorpvp_totalxp,operatorpvp_headshot,operatorpvp_timeplayed,operatorpvp_dbno%s" % (self.spaceid, self.platform_url, self.id, operator_key))
if not "results" in data or not self.id in data["results"]:
raise InvalidRequest("Missing results key in returned JSON object %s" % str(data))
data = data["results"][self.id]
data = {x.split(":")[0].split("_")[1]: data[x] for x in data if x is not None and location in x}
if operator_key:
data["__statistic_name"] = operator_key.split("_")[1]
#if len(data) < 5:
# raise InvalidRequest("invalid number of results for operator in JSON object %s" % data)
oper = Operator(operator, data)
self.operators[operator] = oper
return oper | python | def load_operator(self, operator):
location = yield from self.auth.get_operator_index(operator)
if location is None:
raise ValueError("invalid operator %s" % operator)
operator_key = yield from self.auth.get_operator_statistic(operator)
if operator_key is not None:
operator_key = "," + operator_key
else:
operator_key = ""
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/playerstats2/statistics?populations=%s&statistics=operatorpvp_kills,operatorpvp_death,operatorpvp_roundwon,operatorpvp_roundlost,operatorpvp_meleekills,operatorpvp_totalxp,operatorpvp_headshot,operatorpvp_timeplayed,operatorpvp_dbno%s" % (self.spaceid, self.platform_url, self.id, operator_key))
if not "results" in data or not self.id in data["results"]:
raise InvalidRequest("Missing results key in returned JSON object %s" % str(data))
data = data["results"][self.id]
data = {x.split(":")[0].split("_")[1]: data[x] for x in data if x is not None and location in x}
if operator_key:
data["__statistic_name"] = operator_key.split("_")[1]
oper = Operator(operator, data)
self.operators[operator] = oper
return oper | [
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| |coro|
Loads the players stats for the operator
Parameters
----------
operator : str
the name of the operator
Returns
-------
:class:`Operator`
the operator object found | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1215-L1256 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.get_operator | def get_operator(self, operator):
"""|coro|
Checks the players stats for this operator, only loading them if they haven't already been found
Parameters
----------
operator : str
the name of the operator
Returns
-------
:class:`Operator`
the operator object found"""
if operator in self.operators:
return self.operators[operator]
result = yield from self.load_operator(operator)
return result | python | def get_operator(self, operator):
if operator in self.operators:
return self.operators[operator]
result = yield from self.load_operator(operator)
return result | [
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| |coro|
Checks the players stats for this operator, only loading them if they haven't already been found
Parameters
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operator : str
the name of the operator
Returns
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:class:`Operator`
the operator object found | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1259-L1277 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_weapons | def load_weapons(self):
"""|coro|
Load the players weapon stats
Returns
-------
list[:class:`Weapon`]
list of all the weapon objects found"""
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/playerstats2/statistics?populations=%s&statistics=weapontypepvp_kills,weapontypepvp_headshot,weapontypepvp_bulletfired,weapontypepvp_bullethit" % (self.spaceid, self.platform_url, self.id))
if not "results" in data or not self.id in data["results"]:
raise InvalidRequest("Missing key results in returned JSON object %s" % str(data))
data = data["results"][self.id]
self.weapons = [Weapon(i) for i in range(7)]
for x in data:
spl = x.split(":")
category = spl[0].split("_")[1]
try:
weapontype = int(spl[1]) - 1
weapon = self.weapons[weapontype]
if category == "kills": weapon.kills = data[x]
elif category == "headshot": weapon.headshots = data[x]
elif category == "bulletfired": weapon.shots = data[x]
elif category == "bullethit": weapon.hits = data[x]
except (ValueError, TypeError, IndexError):
pass
return self.weapons | python | def load_weapons(self):
data = yield from self.auth.get("https://public-ubiservices.ubi.com/v1/spaces/%s/sandboxes/%s/playerstats2/statistics?populations=%s&statistics=weapontypepvp_kills,weapontypepvp_headshot,weapontypepvp_bulletfired,weapontypepvp_bullethit" % (self.spaceid, self.platform_url, self.id))
if not "results" in data or not self.id in data["results"]:
raise InvalidRequest("Missing key results in returned JSON object %s" % str(data))
data = data["results"][self.id]
self.weapons = [Weapon(i) for i in range(7)]
for x in data:
spl = x.split(":")
category = spl[0].split("_")[1]
try:
weapontype = int(spl[1]) - 1
weapon = self.weapons[weapontype]
if category == "kills": weapon.kills = data[x]
elif category == "headshot": weapon.headshots = data[x]
elif category == "bulletfired": weapon.shots = data[x]
elif category == "bullethit": weapon.hits = data[x]
except (ValueError, TypeError, IndexError):
pass
return self.weapons | [
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| |coro|
Load the players weapon stats
Returns
-------
list[:class:`Weapon`]
list of all the weapon objects found | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1280-L1310 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_gamemodes | def load_gamemodes(self):
"""|coro|
Loads the players gamemode stats
Returns
-------
dict
dict of all the gamemodes found (gamemode_name: :class:`Gamemode`)"""
stats = yield from self._fetch_statistics("secureareapvp_matchwon", "secureareapvp_matchlost", "secureareapvp_matchplayed",
"secureareapvp_bestscore", "rescuehostagepvp_matchwon", "rescuehostagepvp_matchlost",
"rescuehostagepvp_matchplayed", "rescuehostagepvp_bestscore", "plantbombpvp_matchwon",
"plantbombpvp_matchlost", "plantbombpvp_matchplayed", "plantbombpvp_bestscore",
"generalpvp_servershacked", "generalpvp_serverdefender", "generalpvp_serveraggression",
"generalpvp_hostagerescue", "generalpvp_hostagedefense")
self.gamemodes = {x: Gamemode(x) for x in GamemodeNames}
for name in self.gamemodes:
statname, gamemode = name + "pvp_", self.gamemodes[name]
gamemode.best_score = stats.get(statname + "bestscore", 0)
gamemode.lost = stats.get(statname + "matchlost", 0)
gamemode.won = stats.get(statname + "matchwon", 0)
gamemode.played = stats.get(statname + "matchplayed", 0)
if name == "securearea":
gamemode.areas_secured = stats.get("generalpvp_servershacked", 0)
gamemode.areas_defended = stats.get("generalpvp_serverdefender", 0)
gamemode.areas_contested = stats.get("generalpvp_serveraggression", 0)
elif name == "rescuehostage":
gamemode.hostages_rescued = stats.get("generalpvp_hostagerescue", 0)
gamemode.hostages_defended = stats.get("generalpvp_hostagedefense", 0)
return self.gamemodes | python | def load_gamemodes(self):
stats = yield from self._fetch_statistics("secureareapvp_matchwon", "secureareapvp_matchlost", "secureareapvp_matchplayed",
"secureareapvp_bestscore", "rescuehostagepvp_matchwon", "rescuehostagepvp_matchlost",
"rescuehostagepvp_matchplayed", "rescuehostagepvp_bestscore", "plantbombpvp_matchwon",
"plantbombpvp_matchlost", "plantbombpvp_matchplayed", "plantbombpvp_bestscore",
"generalpvp_servershacked", "generalpvp_serverdefender", "generalpvp_serveraggression",
"generalpvp_hostagerescue", "generalpvp_hostagedefense")
self.gamemodes = {x: Gamemode(x) for x in GamemodeNames}
for name in self.gamemodes:
statname, gamemode = name + "pvp_", self.gamemodes[name]
gamemode.best_score = stats.get(statname + "bestscore", 0)
gamemode.lost = stats.get(statname + "matchlost", 0)
gamemode.won = stats.get(statname + "matchwon", 0)
gamemode.played = stats.get(statname + "matchplayed", 0)
if name == "securearea":
gamemode.areas_secured = stats.get("generalpvp_servershacked", 0)
gamemode.areas_defended = stats.get("generalpvp_serverdefender", 0)
gamemode.areas_contested = stats.get("generalpvp_serveraggression", 0)
elif name == "rescuehostage":
gamemode.hostages_rescued = stats.get("generalpvp_hostagerescue", 0)
gamemode.hostages_defended = stats.get("generalpvp_hostagedefense", 0)
return self.gamemodes | [
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| |coro|
Loads the players gamemode stats
Returns
-------
dict
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"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1327-L1363 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_general | def load_general(self):
"""|coro|
Loads the players general stats"""
stats = yield from self._fetch_statistics("generalpvp_timeplayed", "generalpvp_matchplayed", "generalpvp_matchwon",
"generalpvp_matchlost", "generalpvp_kills", "generalpvp_death",
"generalpvp_bullethit", "generalpvp_bulletfired", "generalpvp_killassists",
"generalpvp_revive", "generalpvp_headshot", "generalpvp_penetrationkills",
"generalpvp_meleekills", "generalpvp_dbnoassists", "generalpvp_suicide",
"generalpvp_barricadedeployed", "generalpvp_reinforcementdeploy", "generalpvp_totalxp",
"generalpvp_rappelbreach", "generalpvp_distancetravelled", "generalpvp_revivedenied",
"generalpvp_dbno", "generalpvp_gadgetdestroy", "generalpvp_blindkills")
statname = "generalpvp_"
self.deaths = stats.get(statname + "death", 0)
self.penetration_kills = stats.get(statname + "penetrationkills", 0)
self.matches_won = stats.get(statname + "matchwon", 0)
self.bullets_hit = stats.get(statname + "bullethit", 0)
self.melee_kills = stats.get(statname + "meleekills", 0)
self.bullets_fired = stats.get(statname + "bulletfired", 0)
self.matches_played = stats.get(statname + "matchplayed", 0)
self.kill_assists = stats.get(statname + "killassists", 0)
self.time_played = stats.get(statname + "timeplayed", 0)
self.revives = stats.get(statname + "revive", 0)
self.kills = stats.get(statname + "kills", 0)
self.headshots = stats.get(statname + "headshot", 0)
self.matches_lost = stats.get(statname + "matchlost", 0)
self.dbno_assists = stats.get(statname + "dbnoassists", 0)
self.suicides = stats.get(statname + "suicide", 0)
self.barricades_deployed = stats.get(statname + "barricadedeployed", 0)
self.reinforcements_deployed = stats.get(statname + "reinforcementdeploy", 0)
self.total_xp = stats.get(statname + "totalxp", 0)
self.rappel_breaches = stats.get(statname + "rappelbreach", 0)
self.distance_travelled = stats.get(statname + "distancetravelled", 0)
self.revives_denied = stats.get(statname + "revivedenied", 0)
self.dbnos = stats.get(statname + "dbno", 0)
self.gadgets_destroyed = stats.get(statname + "gadgetdestroy", 0)
self.blind_kills = stats.get(statname + "blindkills") | python | def load_general(self):
stats = yield from self._fetch_statistics("generalpvp_timeplayed", "generalpvp_matchplayed", "generalpvp_matchwon",
"generalpvp_matchlost", "generalpvp_kills", "generalpvp_death",
"generalpvp_bullethit", "generalpvp_bulletfired", "generalpvp_killassists",
"generalpvp_revive", "generalpvp_headshot", "generalpvp_penetrationkills",
"generalpvp_meleekills", "generalpvp_dbnoassists", "generalpvp_suicide",
"generalpvp_barricadedeployed", "generalpvp_reinforcementdeploy", "generalpvp_totalxp",
"generalpvp_rappelbreach", "generalpvp_distancetravelled", "generalpvp_revivedenied",
"generalpvp_dbno", "generalpvp_gadgetdestroy", "generalpvp_blindkills")
statname = "generalpvp_"
self.deaths = stats.get(statname + "death", 0)
self.penetration_kills = stats.get(statname + "penetrationkills", 0)
self.matches_won = stats.get(statname + "matchwon", 0)
self.bullets_hit = stats.get(statname + "bullethit", 0)
self.melee_kills = stats.get(statname + "meleekills", 0)
self.bullets_fired = stats.get(statname + "bulletfired", 0)
self.matches_played = stats.get(statname + "matchplayed", 0)
self.kill_assists = stats.get(statname + "killassists", 0)
self.time_played = stats.get(statname + "timeplayed", 0)
self.revives = stats.get(statname + "revive", 0)
self.kills = stats.get(statname + "kills", 0)
self.headshots = stats.get(statname + "headshot", 0)
self.matches_lost = stats.get(statname + "matchlost", 0)
self.dbno_assists = stats.get(statname + "dbnoassists", 0)
self.suicides = stats.get(statname + "suicide", 0)
self.barricades_deployed = stats.get(statname + "barricadedeployed", 0)
self.reinforcements_deployed = stats.get(statname + "reinforcementdeploy", 0)
self.total_xp = stats.get(statname + "totalxp", 0)
self.rappel_breaches = stats.get(statname + "rappelbreach", 0)
self.distance_travelled = stats.get(statname + "distancetravelled", 0)
self.revives_denied = stats.get(statname + "revivedenied", 0)
self.dbnos = stats.get(statname + "dbno", 0)
self.gadgets_destroyed = stats.get(statname + "gadgetdestroy", 0)
self.blind_kills = stats.get(statname + "blindkills") | [
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| |coro|
Loads the players general stats | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1380-L1418 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_queues | def load_queues(self):
"""|coro|
Loads the players game queues"""
stats = yield from self._fetch_statistics("casualpvp_matchwon", "casualpvp_matchlost", "casualpvp_timeplayed",
"casualpvp_matchplayed", "casualpvp_kills", "casualpvp_death",
"rankedpvp_matchwon", "rankedpvp_matchlost", "rankedpvp_timeplayed",
"rankedpvp_matchplayed", "rankedpvp_kills", "rankedpvp_death")
self.ranked = GameQueue("ranked")
self.casual = GameQueue("casual")
for gq in (self.ranked, self.casual):
statname = gq.name + "pvp_"
gq.won = stats.get(statname + "matchwon", 0)
gq.lost = stats.get(statname + "matchlost", 0)
gq.time_played = stats.get(statname + "timeplayed", 0)
gq.played = stats.get(statname + "matchplayed", 0)
gq.kills = stats.get(statname + "kills", 0)
gq.deaths = stats.get(statname + "death", 0) | python | def load_queues(self):
stats = yield from self._fetch_statistics("casualpvp_matchwon", "casualpvp_matchlost", "casualpvp_timeplayed",
"casualpvp_matchplayed", "casualpvp_kills", "casualpvp_death",
"rankedpvp_matchwon", "rankedpvp_matchlost", "rankedpvp_timeplayed",
"rankedpvp_matchplayed", "rankedpvp_kills", "rankedpvp_death")
self.ranked = GameQueue("ranked")
self.casual = GameQueue("casual")
for gq in (self.ranked, self.casual):
statname = gq.name + "pvp_"
gq.won = stats.get(statname + "matchwon", 0)
gq.lost = stats.get(statname + "matchlost", 0)
gq.time_played = stats.get(statname + "timeplayed", 0)
gq.played = stats.get(statname + "matchplayed", 0)
gq.kills = stats.get(statname + "kills", 0)
gq.deaths = stats.get(statname + "death", 0) | [
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| |coro|
Loads the players game queues | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1430-L1451 |
billy-yoyo/RainbowSixSiege-Python-API | r6sapi/r6sapi.py | Player.load_terrohunt | def load_terrohunt(self):
"""|coro|
Loads the player's general stats for terrorist hunt"""
stats = yield from self._fetch_statistics("generalpve_dbnoassists", "generalpve_death", "generalpve_revive",
"generalpve_matchwon", "generalpve_suicide", "generalpve_servershacked",
"generalpve_serverdefender", "generalpve_barricadedeployed", "generalpve_reinforcementdeploy",
"generalpve_kills", "generalpve_hostagedefense", "generalpve_bulletfired",
"generalpve_matchlost", "generalpve_killassists", "generalpve_totalxp",
"generalpve_hostagerescue", "generalpve_penetrationkills", "generalpve_meleekills",
"generalpve_rappelbreach", "generalpve_distancetravelled", "generalpve_matchplayed",
"generalpve_serveraggression", "generalpve_timeplayed", "generalpve_revivedenied",
"generalpve_dbno", "generalpve_bullethit", "generalpve_blindkills", "generalpve_headshot",
"generalpve_gadgetdestroy", "generalpve_accuracy")
self.terrorist_hunt = GameQueue("terrohunt")
statname = "generalpve_"
self.terrorist_hunt.deaths = stats.get(statname + "death", 0)
self.terrorist_hunt.penetration_kills = stats.get(statname + "penetrationkills", 0)
self.terrorist_hunt.matches_won = stats.get(statname + "matchwon", 0)
self.terrorist_hunt.bullets_hit = stats.get(statname + "bullethit", 0)
self.terrorist_hunt.melee_kills = stats.get(statname + "meleekills", 0)
self.terrorist_hunt.bullets_fired = stats.get(statname + "bulletfired", 0)
self.terrorist_hunt.matches_played = stats.get(statname + "matchplayed", 0)
self.terrorist_hunt.kill_assists = stats.get(statname + "killassists", 0)
self.terrorist_hunt.time_played = stats.get(statname + "timeplayed", 0)
self.terrorist_hunt.revives = stats.get(statname + "revive", 0)
self.terrorist_hunt.kills = stats.get(statname + "kills", 0)
self.terrorist_hunt.headshots = stats.get(statname + "headshot", 0)
self.terrorist_hunt.matches_lost = stats.get(statname + "matchlost", 0)
self.terrorist_hunt.dbno_assists = stats.get(statname + "dbnoassists", 0)
self.terrorist_hunt.suicides = stats.get(statname + "suicide", 0)
self.terrorist_hunt.barricades_deployed = stats.get(statname + "barricadedeployed", 0)
self.terrorist_hunt.reinforcements_deployed = stats.get(statname + "reinforcementdeploy", 0)
self.terrorist_hunt.total_xp = stats.get(statname + "totalxp", 0)
self.terrorist_hunt.rappel_breaches = stats.get(statname + "rappelbreach", 0)
self.terrorist_hunt.distance_travelled = stats.get(statname + "distancetravelled", 0)
self.terrorist_hunt.revives_denied = stats.get(statname + "revivedenied", 0)
self.terrorist_hunt.dbnos = stats.get(statname + "dbno", 0)
self.terrorist_hunt.gadgets_destroyed = stats.get(statname + "gadgetdestroy", 0)
self.terrorist_hunt.areas_secured = stats.get(statname + "servershacked", 0)
self.terrorist_hunt.areas_defended = stats.get(statname + "serverdefender", 0)
self.terrorist_hunt.areas_contested = stats.get(statname + "serveraggression", 0)
self.terrorist_hunt.hostages_rescued = stats.get(statname + "hostagerescue", 0)
self.terrorist_hunt.hostages_defended = stats.get(statname + "hostagedefense", 0)
self.terrorist_hunt.blind_kills = stats.get(statname + "blindkills", 0)
return self.terrorist_hunt | python | def load_terrohunt(self):
stats = yield from self._fetch_statistics("generalpve_dbnoassists", "generalpve_death", "generalpve_revive",
"generalpve_matchwon", "generalpve_suicide", "generalpve_servershacked",
"generalpve_serverdefender", "generalpve_barricadedeployed", "generalpve_reinforcementdeploy",
"generalpve_kills", "generalpve_hostagedefense", "generalpve_bulletfired",
"generalpve_matchlost", "generalpve_killassists", "generalpve_totalxp",
"generalpve_hostagerescue", "generalpve_penetrationkills", "generalpve_meleekills",
"generalpve_rappelbreach", "generalpve_distancetravelled", "generalpve_matchplayed",
"generalpve_serveraggression", "generalpve_timeplayed", "generalpve_revivedenied",
"generalpve_dbno", "generalpve_bullethit", "generalpve_blindkills", "generalpve_headshot",
"generalpve_gadgetdestroy", "generalpve_accuracy")
self.terrorist_hunt = GameQueue("terrohunt")
statname = "generalpve_"
self.terrorist_hunt.deaths = stats.get(statname + "death", 0)
self.terrorist_hunt.penetration_kills = stats.get(statname + "penetrationkills", 0)
self.terrorist_hunt.matches_won = stats.get(statname + "matchwon", 0)
self.terrorist_hunt.bullets_hit = stats.get(statname + "bullethit", 0)
self.terrorist_hunt.melee_kills = stats.get(statname + "meleekills", 0)
self.terrorist_hunt.bullets_fired = stats.get(statname + "bulletfired", 0)
self.terrorist_hunt.matches_played = stats.get(statname + "matchplayed", 0)
self.terrorist_hunt.kill_assists = stats.get(statname + "killassists", 0)
self.terrorist_hunt.time_played = stats.get(statname + "timeplayed", 0)
self.terrorist_hunt.revives = stats.get(statname + "revive", 0)
self.terrorist_hunt.kills = stats.get(statname + "kills", 0)
self.terrorist_hunt.headshots = stats.get(statname + "headshot", 0)
self.terrorist_hunt.matches_lost = stats.get(statname + "matchlost", 0)
self.terrorist_hunt.dbno_assists = stats.get(statname + "dbnoassists", 0)
self.terrorist_hunt.suicides = stats.get(statname + "suicide", 0)
self.terrorist_hunt.barricades_deployed = stats.get(statname + "barricadedeployed", 0)
self.terrorist_hunt.reinforcements_deployed = stats.get(statname + "reinforcementdeploy", 0)
self.terrorist_hunt.total_xp = stats.get(statname + "totalxp", 0)
self.terrorist_hunt.rappel_breaches = stats.get(statname + "rappelbreach", 0)
self.terrorist_hunt.distance_travelled = stats.get(statname + "distancetravelled", 0)
self.terrorist_hunt.revives_denied = stats.get(statname + "revivedenied", 0)
self.terrorist_hunt.dbnos = stats.get(statname + "dbno", 0)
self.terrorist_hunt.gadgets_destroyed = stats.get(statname + "gadgetdestroy", 0)
self.terrorist_hunt.areas_secured = stats.get(statname + "servershacked", 0)
self.terrorist_hunt.areas_defended = stats.get(statname + "serverdefender", 0)
self.terrorist_hunt.areas_contested = stats.get(statname + "serveraggression", 0)
self.terrorist_hunt.hostages_rescued = stats.get(statname + "hostagerescue", 0)
self.terrorist_hunt.hostages_defended = stats.get(statname + "hostagedefense", 0)
self.terrorist_hunt.blind_kills = stats.get(statname + "blindkills", 0)
return self.terrorist_hunt | [
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"terrorist_hunt",
".",
"revives",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"revive\"",
",",
"0",
")",
"self",
".",
"terrorist_hunt",
".",
"kills",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"kills\"",
",",
"0",
")",
"self",
".",
"terrorist_hunt",
".",
"headshots",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"headshot\"",
",",
"0",
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".",
"terrorist_hunt",
".",
"matches_lost",
"=",
"stats",
".",
"get",
"(",
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"+",
"\"matchlost\"",
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"0",
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".",
"terrorist_hunt",
".",
"dbno_assists",
"=",
"stats",
".",
"get",
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"\"dbnoassists\"",
",",
"0",
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"self",
".",
"terrorist_hunt",
".",
"suicides",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"suicide\"",
",",
"0",
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"self",
".",
"terrorist_hunt",
".",
"barricades_deployed",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"barricadedeployed\"",
",",
"0",
")",
"self",
".",
"terrorist_hunt",
".",
"reinforcements_deployed",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"reinforcementdeploy\"",
",",
"0",
")",
"self",
".",
"terrorist_hunt",
".",
"total_xp",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"totalxp\"",
",",
"0",
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"self",
".",
"terrorist_hunt",
".",
"rappel_breaches",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"rappelbreach\"",
",",
"0",
")",
"self",
".",
"terrorist_hunt",
".",
"distance_travelled",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"distancetravelled\"",
",",
"0",
")",
"self",
".",
"terrorist_hunt",
".",
"revives_denied",
"=",
"stats",
".",
"get",
"(",
"statname",
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"\"revivedenied\"",
",",
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".",
"dbnos",
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"stats",
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"\"dbno\"",
",",
"0",
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".",
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".",
"gadgets_destroyed",
"=",
"stats",
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"get",
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"statname",
"+",
"\"gadgetdestroy\"",
",",
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".",
"areas_secured",
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"stats",
".",
"get",
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"\"servershacked\"",
",",
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".",
"areas_defended",
"=",
"stats",
".",
"get",
"(",
"statname",
"+",
"\"serverdefender\"",
",",
"0",
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"areas_contested",
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"stats",
".",
"get",
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"statname",
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".",
"hostages_rescued",
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"blind_kills",
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"(",
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"+",
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",",
"0",
")",
"return",
"self",
".",
"terrorist_hunt"
]
| |coro|
Loads the player's general stats for terrorist hunt | [
"|coro|"
]
| train | https://github.com/billy-yoyo/RainbowSixSiege-Python-API/blob/9860fdfd9a78aabd977eaa71b0a4ab4ed69e94d0/r6sapi/r6sapi.py#L1463-L1511 |
jepcastelein/marketo-rest-python | marketorestpython/client.py | MarketoClient.execute | def execute(self, method, *args, **kargs):
result = None
'''
max 10 rechecks
'''
for i in range(0, 10):
try:
method_map = {
'get_lead_by_id': self.get_lead_by_id,
'get_multiple_leads_by_filter_type': self.get_multiple_leads_by_filter_type,
'get_multiple_leads_by_list_id': self.get_multiple_leads_by_list_id,
'get_multiple_leads_by_list_id_yield': self.get_multiple_leads_by_list_id_yield,
'get_multiple_leads_by_program_id': self.get_multiple_leads_by_program_id,
'get_multiple_leads_by_program_id_yield': self.get_multiple_leads_by_program_id_yield,
'change_lead_program_status': self.change_lead_program_status,
'create_update_leads': self.create_update_leads,
'associate_lead': self.associate_lead,
'push_lead': self.push_lead,
'merge_lead': self.merge_lead,
'get_lead_partitions': self.get_lead_partitions,
'create_list': self.create_list,
'update_list': self.update_list,
'delete_list': self.delete_list,
'get_list_by_id': self.get_list_by_id,
'get_list_by_name': self.get_list_by_name,
'get_multiple_lists': self.get_multiple_lists,
'browse_lists': self.browse_lists,
'add_leads_to_list': self.add_leads_to_list,
'remove_leads_from_list': self.remove_leads_from_list,
'member_of_list': self.member_of_list,
'get_campaign_by_id': self.get_campaign_by_id,
'get_multiple_campaigns': self.get_multiple_campaigns,
'schedule_campaign': self.schedule_campaign,
'request_campaign': self.request_campaign,
'import_lead': self.import_lead,
'get_import_lead_status': self.get_import_lead_status,
'get_import_failure_file': self.get_import_failure_file,
'get_import_warning_file': self.get_import_warning_file,
'describe': self.describe,
'get_activity_types': self.get_activity_types,
'get_paging_token': self.get_paging_token,
'get_lead_activities': self.get_lead_activities,
'get_lead_activities_yield': self.get_lead_activities_yield,
'get_lead_changes': self.get_lead_changes,
'get_lead_changes_yield': self.get_lead_changes_yield,
'add_custom_activities': self.add_custom_activities,
'get_daily_usage': self.get_daily_usage,
'get_last_7_days_usage': self.get_last_7_days_usage,
'get_daily_errors': self.get_daily_errors,
'get_last_7_days_errors': self.get_last_7_days_errors,
'delete_lead': self.delete_lead,
'get_deleted_leads': self.get_deleted_leads,
'update_leads_partition': self.update_leads_partition,
'create_folder': self.create_folder,
'get_folder_by_id': self.get_folder_by_id,
'get_folder_by_name': self.get_folder_by_name,
'get_folder_contents': self.get_folder_contents,
'update_folder': self.update_folder,
'delete_folder': self.delete_folder,
'browse_folders': self.browse_folders,
'create_token': self.create_token,
'get_tokens': self.get_tokens,
'delete_tokens': self.delete_tokens,
'create_email_template': self.create_email_template,
'get_email_template_by_id': self.get_email_template_by_id,
'get_email_template_by_name': self.get_email_template_by_name,
'update_email_template': self.update_email_template,
'delete_email_template': self.delete_email_template,
'get_email_templates': self.get_email_templates,
'get_email_templates_yield': self.get_email_templates_yield,
'get_email_template_content': self.get_email_template_content,
'update_email_template_content': self.update_email_template_content,
'approve_email_template': self.approve_email_template,
'unapprove_email_template': self.unapprove_email_template,
'discard_email_template_draft': self.discard_email_template_draft,
'clone_email_template': self.clone_email_template,
'create_email': self.create_email,
'get_email_by_id': self.get_email_by_id,
'get_email_by_name': self.get_email_by_name,
'delete_email': self.delete_email,
'update_email': self.update_email,
'get_emails': self.get_emails,
'get_emails_yield': self.get_emails_yield,
'get_email_content': self.get_email_content,
'update_email_content': self.update_email_content,
'update_email_content_in_editable_section': self.update_email_content_in_editable_section,
'get_email_dynamic_content': self.get_email_dynamic_content,
'update_email_dynamic_content': self.update_email_dynamic_content,
'approve_email': self.approve_email,
'unapprove_email': self.unapprove_email,
'discard_email_draft': self.discard_email_draft,
'clone_email': self.clone_email,
'send_sample_email': self.send_sample_email,
'get_email_full_content': self.get_email_full_content,
'create_landing_page': self.create_landing_page,
'get_landing_page_by_id': self.get_landing_page_by_id,
'get_landing_page_by_name': self.get_landing_page_by_name,
'delete_landing_page': self.delete_landing_page,
'update_landing_page': self.update_landing_page,
'get_landing_pages': self.get_landing_pages,
'get_landing_pages_yield': self.get_landing_pages_yield,
'get_landing_page_content': self.get_landing_page_content,
'create_landing_page_content_section': self.create_landing_page_content_section,
'update_landing_page_content_section': self.update_landing_page_content_section,
'delete_landing_page_content_section': self.delete_landing_page_content_section,
'get_landing_page_dynamic_content': self.get_landing_page_dynamic_content,
'update_landing_page_dynamic_content': self.update_landing_page_dynamic_content,
'approve_landing_page': self.approve_landing_page,
'unapprove_landing_page': self.unapprove_landing_page,
'discard_landing_page_draft': self.discard_landing_page_draft,
'clone_landing_page': self.clone_landing_page,
'create_form': self.create_form,
'get_form_by_id': self.get_form_by_id,
'get_form_by_name': self.get_form_by_name,
'delete_form': self.delete_form,
'update_form': self.update_form,
'get_forms': self.get_forms,
'get_forms_yield': self.get_forms_yield,
'get_form_fields': self.get_form_fields,
'create_form_field': self.create_form_field,
'update_form_field': self.update_form_field,
'delete_form_field': self.delete_form_field,
'approve_form': self.approve_form,
'unapprove_form': self.unapprove_form,
'discard_form_draft': self.discard_form_draft,
'clone_form': self.clone_form,
'create_file': self.create_file,
'get_file_by_id': self.get_file_by_id,
'get_file_by_name': self.get_file_by_name,
'list_files': self.list_files,
'get_files_yield': self.get_files_yield,
'update_file_content': self.update_file_content,
'create_snippet': self.create_snippet,
'get_snippet_by_id': self.get_snippet_by_id,
'delete_snippet': self.delete_snippet,
'update_snippet': self.update_snippet,
'get_snippets': self.get_snippets,
'get_snippets_yield': self.get_snippets_yield,
'get_snippet_content': self.get_snippet_content,
'update_snippet_content': self.update_snippet_content,
'approve_snippet': self.approve_snippet,
'unapprove_snippet': self.unapprove_snippet,
'discard_snippet_draft': self.discard_snippet_draft,
'clone_snippet': self.clone_snippet,
'update_snippet_dynamic_content': self.update_snippet_dynamic_content,
'get_snippet_dynamic_content': self.get_snippet_dynamic_content,
'get_segmentations': self.get_segmentations,
'get_segments': self.get_segments,
'create_landing_page_template': self.create_landing_page_template,
'get_landing_page_template_by_id': self.get_landing_page_template_by_id,
'get_landing_page_template_by_name': self.get_landing_page_template_by_name,
'get_landing_page_templates': self.get_landing_page_templates,
'get_landing_page_templates_yield': self.get_landing_page_templates_yield,
'get_landing_page_template_content': self.get_landing_page_template_content,
'update_landing_page_template_content': self.update_landing_page_template_content,
'update_landing_page_template': self.update_landing_page_template,
'delete_landing_page_template': self.delete_landing_page_template,
'approve_landing_page_template': self.approve_landing_page_template,
'unapprove_landing_page_template': self.unapprove_landing_page_template,
'discard_landing_page_template_draft': self.discard_landing_page_template_draft,
'clone_landing_page_template': self.clone_landing_page_template,
'create_program': self.create_program,
'get_program_by_id': self.get_program_by_id,
'get_program_by_name': self.get_program_by_name,
'get_program_by_tag_type': self.get_program_by_tag_type,
'update_program': self.update_program,
'delete_program': self.delete_program,
'browse_programs': self.browse_programs,
'get_programs_yield': self.get_programs_yield,
'clone_program': self.clone_program,
'approve_program': self.approve_program,
'unapprove_program': self.unapprove_program,
'get_channels': self.get_channels,
'get_channel_by_name': self.get_channel_by_name,
'get_tags': self.get_tags,
'get_tag_by_name': self.get_tag_by_name,
'get_list_of_custom_objects': self.get_list_of_custom_objects,
'describe_custom_object': self.describe_custom_object,
'create_update_custom_objects': self.create_update_custom_objects,
'delete_custom_objects': self.delete_custom_objects,
'get_custom_objects': self.get_custom_objects,
'describe_opportunity': self.describe_opportunity,
'create_update_opportunities': self.create_update_opportunities,
'delete_opportunities': self.delete_opportunities,
'get_opportunities': self.get_opportunities,
'describe_opportunity_role': self.describe_opportunity_role,
'create_update_opportunities_roles': self.create_update_opportunities_roles,
'delete_opportunity_roles': self.delete_opportunity_roles,
'get_opportunity_roles': self.get_opportunity_roles,
'describe_company': self.describe_company,
'create_update_companies': self.create_update_companies,
'delete_companies': self.delete_companies,
'get_companies': self.get_companies,
'describe_sales_person': self.describe_sales_person,
'create_update_sales_persons': self.create_update_sales_persons,
'delete_sales_persons': self.delete_sales_persons,
'get_sales_persons': self.get_sales_persons,
'get_custom_activity_types': self.get_custom_activity_types,
'describe_custom_activity_type': self.describe_custom_activity_type,
'create_custom_activity_type': self.create_custom_activity_type,
'update_custom_activity_type': self.update_custom_activity_type,
'approve_custom_activity_type': self.approve_custom_activity_type,
'create_custom_activity_type_attribute': self.create_custom_activity_type_attribute,
'discard_custom_activity_type_draft': self.discard_custom_activity_type_draft,
'delete_custom_activity_type': self.delete_custom_activity_type,
'update_custom_activity_type_attribute': self.update_custom_activity_type_attribute,
'delete_custom_activity_type_attribute': self.delete_custom_activity_type_attribute,
'get_leads_export_jobs_list': self.get_leads_export_jobs_list,
'get_activities_export_jobs_list': self.get_activities_export_jobs_list,
'create_leads_export_job': self.create_leads_export_job,
'create_activities_export_job': self.create_activities_export_job,
'enqueue_leads_export_job': self.enqueue_leads_export_job,
'enqueue_activities_export_job': self.enqueue_activities_export_job,
'cancel_leads_export_job': self.cancel_leads_export_job,
'cancel_activities_export_job': self.cancel_activities_export_job,
'get_leads_export_job_status': self.get_leads_export_job_status,
'get_activities_export_job_status': self.get_activities_export_job_status,
'get_leads_export_job_file': self.get_leads_export_job_file,
'get_activities_export_job_file': self.get_activities_export_job_file
}
result = method_map[method](*args, **kargs)
except MarketoException as e:
'''
601 -> auth token not valid
602 -> auth token expired
'''
if e.code in ['601', '602']:
self.authenticate()
continue
else:
raise Exception({'message': e.message, 'code': e.code})
break
return result | python | def execute(self, method, *args, **kargs):
result = None
for i in range(0, 10):
try:
method_map = {
'get_lead_by_id': self.get_lead_by_id,
'get_multiple_leads_by_filter_type': self.get_multiple_leads_by_filter_type,
'get_multiple_leads_by_list_id': self.get_multiple_leads_by_list_id,
'get_multiple_leads_by_list_id_yield': self.get_multiple_leads_by_list_id_yield,
'get_multiple_leads_by_program_id': self.get_multiple_leads_by_program_id,
'get_multiple_leads_by_program_id_yield': self.get_multiple_leads_by_program_id_yield,
'change_lead_program_status': self.change_lead_program_status,
'create_update_leads': self.create_update_leads,
'associate_lead': self.associate_lead,
'push_lead': self.push_lead,
'merge_lead': self.merge_lead,
'get_lead_partitions': self.get_lead_partitions,
'create_list': self.create_list,
'update_list': self.update_list,
'delete_list': self.delete_list,
'get_list_by_id': self.get_list_by_id,
'get_list_by_name': self.get_list_by_name,
'get_multiple_lists': self.get_multiple_lists,
'browse_lists': self.browse_lists,
'add_leads_to_list': self.add_leads_to_list,
'remove_leads_from_list': self.remove_leads_from_list,
'member_of_list': self.member_of_list,
'get_campaign_by_id': self.get_campaign_by_id,
'get_multiple_campaigns': self.get_multiple_campaigns,
'schedule_campaign': self.schedule_campaign,
'request_campaign': self.request_campaign,
'import_lead': self.import_lead,
'get_import_lead_status': self.get_import_lead_status,
'get_import_failure_file': self.get_import_failure_file,
'get_import_warning_file': self.get_import_warning_file,
'describe': self.describe,
'get_activity_types': self.get_activity_types,
'get_paging_token': self.get_paging_token,
'get_lead_activities': self.get_lead_activities,
'get_lead_activities_yield': self.get_lead_activities_yield,
'get_lead_changes': self.get_lead_changes,
'get_lead_changes_yield': self.get_lead_changes_yield,
'add_custom_activities': self.add_custom_activities,
'get_daily_usage': self.get_daily_usage,
'get_last_7_days_usage': self.get_last_7_days_usage,
'get_daily_errors': self.get_daily_errors,
'get_last_7_days_errors': self.get_last_7_days_errors,
'delete_lead': self.delete_lead,
'get_deleted_leads': self.get_deleted_leads,
'update_leads_partition': self.update_leads_partition,
'create_folder': self.create_folder,
'get_folder_by_id': self.get_folder_by_id,
'get_folder_by_name': self.get_folder_by_name,
'get_folder_contents': self.get_folder_contents,
'update_folder': self.update_folder,
'delete_folder': self.delete_folder,
'browse_folders': self.browse_folders,
'create_token': self.create_token,
'get_tokens': self.get_tokens,
'delete_tokens': self.delete_tokens,
'create_email_template': self.create_email_template,
'get_email_template_by_id': self.get_email_template_by_id,
'get_email_template_by_name': self.get_email_template_by_name,
'update_email_template': self.update_email_template,
'delete_email_template': self.delete_email_template,
'get_email_templates': self.get_email_templates,
'get_email_templates_yield': self.get_email_templates_yield,
'get_email_template_content': self.get_email_template_content,
'update_email_template_content': self.update_email_template_content,
'approve_email_template': self.approve_email_template,
'unapprove_email_template': self.unapprove_email_template,
'discard_email_template_draft': self.discard_email_template_draft,
'clone_email_template': self.clone_email_template,
'create_email': self.create_email,
'get_email_by_id': self.get_email_by_id,
'get_email_by_name': self.get_email_by_name,
'delete_email': self.delete_email,
'update_email': self.update_email,
'get_emails': self.get_emails,
'get_emails_yield': self.get_emails_yield,
'get_email_content': self.get_email_content,
'update_email_content': self.update_email_content,
'update_email_content_in_editable_section': self.update_email_content_in_editable_section,
'get_email_dynamic_content': self.get_email_dynamic_content,
'update_email_dynamic_content': self.update_email_dynamic_content,
'approve_email': self.approve_email,
'unapprove_email': self.unapprove_email,
'discard_email_draft': self.discard_email_draft,
'clone_email': self.clone_email,
'send_sample_email': self.send_sample_email,
'get_email_full_content': self.get_email_full_content,
'create_landing_page': self.create_landing_page,
'get_landing_page_by_id': self.get_landing_page_by_id,
'get_landing_page_by_name': self.get_landing_page_by_name,
'delete_landing_page': self.delete_landing_page,
'update_landing_page': self.update_landing_page,
'get_landing_pages': self.get_landing_pages,
'get_landing_pages_yield': self.get_landing_pages_yield,
'get_landing_page_content': self.get_landing_page_content,
'create_landing_page_content_section': self.create_landing_page_content_section,
'update_landing_page_content_section': self.update_landing_page_content_section,
'delete_landing_page_content_section': self.delete_landing_page_content_section,
'get_landing_page_dynamic_content': self.get_landing_page_dynamic_content,
'update_landing_page_dynamic_content': self.update_landing_page_dynamic_content,
'approve_landing_page': self.approve_landing_page,
'unapprove_landing_page': self.unapprove_landing_page,
'discard_landing_page_draft': self.discard_landing_page_draft,
'clone_landing_page': self.clone_landing_page,
'create_form': self.create_form,
'get_form_by_id': self.get_form_by_id,
'get_form_by_name': self.get_form_by_name,
'delete_form': self.delete_form,
'update_form': self.update_form,
'get_forms': self.get_forms,
'get_forms_yield': self.get_forms_yield,
'get_form_fields': self.get_form_fields,
'create_form_field': self.create_form_field,
'update_form_field': self.update_form_field,
'delete_form_field': self.delete_form_field,
'approve_form': self.approve_form,
'unapprove_form': self.unapprove_form,
'discard_form_draft': self.discard_form_draft,
'clone_form': self.clone_form,
'create_file': self.create_file,
'get_file_by_id': self.get_file_by_id,
'get_file_by_name': self.get_file_by_name,
'list_files': self.list_files,
'get_files_yield': self.get_files_yield,
'update_file_content': self.update_file_content,
'create_snippet': self.create_snippet,
'get_snippet_by_id': self.get_snippet_by_id,
'delete_snippet': self.delete_snippet,
'update_snippet': self.update_snippet,
'get_snippets': self.get_snippets,
'get_snippets_yield': self.get_snippets_yield,
'get_snippet_content': self.get_snippet_content,
'update_snippet_content': self.update_snippet_content,
'approve_snippet': self.approve_snippet,
'unapprove_snippet': self.unapprove_snippet,
'discard_snippet_draft': self.discard_snippet_draft,
'clone_snippet': self.clone_snippet,
'update_snippet_dynamic_content': self.update_snippet_dynamic_content,
'get_snippet_dynamic_content': self.get_snippet_dynamic_content,
'get_segmentations': self.get_segmentations,
'get_segments': self.get_segments,
'create_landing_page_template': self.create_landing_page_template,
'get_landing_page_template_by_id': self.get_landing_page_template_by_id,
'get_landing_page_template_by_name': self.get_landing_page_template_by_name,
'get_landing_page_templates': self.get_landing_page_templates,
'get_landing_page_templates_yield': self.get_landing_page_templates_yield,
'get_landing_page_template_content': self.get_landing_page_template_content,
'update_landing_page_template_content': self.update_landing_page_template_content,
'update_landing_page_template': self.update_landing_page_template,
'delete_landing_page_template': self.delete_landing_page_template,
'approve_landing_page_template': self.approve_landing_page_template,
'unapprove_landing_page_template': self.unapprove_landing_page_template,
'discard_landing_page_template_draft': self.discard_landing_page_template_draft,
'clone_landing_page_template': self.clone_landing_page_template,
'create_program': self.create_program,
'get_program_by_id': self.get_program_by_id,
'get_program_by_name': self.get_program_by_name,
'get_program_by_tag_type': self.get_program_by_tag_type,
'update_program': self.update_program,
'delete_program': self.delete_program,
'browse_programs': self.browse_programs,
'get_programs_yield': self.get_programs_yield,
'clone_program': self.clone_program,
'approve_program': self.approve_program,
'unapprove_program': self.unapprove_program,
'get_channels': self.get_channels,
'get_channel_by_name': self.get_channel_by_name,
'get_tags': self.get_tags,
'get_tag_by_name': self.get_tag_by_name,
'get_list_of_custom_objects': self.get_list_of_custom_objects,
'describe_custom_object': self.describe_custom_object,
'create_update_custom_objects': self.create_update_custom_objects,
'delete_custom_objects': self.delete_custom_objects,
'get_custom_objects': self.get_custom_objects,
'describe_opportunity': self.describe_opportunity,
'create_update_opportunities': self.create_update_opportunities,
'delete_opportunities': self.delete_opportunities,
'get_opportunities': self.get_opportunities,
'describe_opportunity_role': self.describe_opportunity_role,
'create_update_opportunities_roles': self.create_update_opportunities_roles,
'delete_opportunity_roles': self.delete_opportunity_roles,
'get_opportunity_roles': self.get_opportunity_roles,
'describe_company': self.describe_company,
'create_update_companies': self.create_update_companies,
'delete_companies': self.delete_companies,
'get_companies': self.get_companies,
'describe_sales_person': self.describe_sales_person,
'create_update_sales_persons': self.create_update_sales_persons,
'delete_sales_persons': self.delete_sales_persons,
'get_sales_persons': self.get_sales_persons,
'get_custom_activity_types': self.get_custom_activity_types,
'describe_custom_activity_type': self.describe_custom_activity_type,
'create_custom_activity_type': self.create_custom_activity_type,
'update_custom_activity_type': self.update_custom_activity_type,
'approve_custom_activity_type': self.approve_custom_activity_type,
'create_custom_activity_type_attribute': self.create_custom_activity_type_attribute,
'discard_custom_activity_type_draft': self.discard_custom_activity_type_draft,
'delete_custom_activity_type': self.delete_custom_activity_type,
'update_custom_activity_type_attribute': self.update_custom_activity_type_attribute,
'delete_custom_activity_type_attribute': self.delete_custom_activity_type_attribute,
'get_leads_export_jobs_list': self.get_leads_export_jobs_list,
'get_activities_export_jobs_list': self.get_activities_export_jobs_list,
'create_leads_export_job': self.create_leads_export_job,
'create_activities_export_job': self.create_activities_export_job,
'enqueue_leads_export_job': self.enqueue_leads_export_job,
'enqueue_activities_export_job': self.enqueue_activities_export_job,
'cancel_leads_export_job': self.cancel_leads_export_job,
'cancel_activities_export_job': self.cancel_activities_export_job,
'get_leads_export_job_status': self.get_leads_export_job_status,
'get_activities_export_job_status': self.get_activities_export_job_status,
'get_leads_export_job_file': self.get_leads_export_job_file,
'get_activities_export_job_file': self.get_activities_export_job_file
}
result = method_map[method](*args, **kargs)
except MarketoException as e:
if e.code in ['601', '602']:
self.authenticate()
continue
else:
raise Exception({'message': e.message, 'code': e.code})
break
return result | [
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| train | https://github.com/jepcastelein/marketo-rest-python/blob/af9592b7f6dea8166dca1a0223ebc1066c25ca93/marketorestpython/client.py#L46-L280 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/wrapper.py | load_libdmtx | def load_libdmtx():
"""Loads the libdmtx shared library.
Populates the globals LIBDMTX and EXTERNAL_DEPENDENCIES.
"""
global LIBDMTX
global EXTERNAL_DEPENDENCIES
if not LIBDMTX:
LIBDMTX = dmtx_library.load()
EXTERNAL_DEPENDENCIES = [LIBDMTX]
return LIBDMTX | python | def load_libdmtx():
global LIBDMTX
global EXTERNAL_DEPENDENCIES
if not LIBDMTX:
LIBDMTX = dmtx_library.load()
EXTERNAL_DEPENDENCIES = [LIBDMTX]
return LIBDMTX | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/wrapper.py#L32-L43 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/wrapper.py | libdmtx_function | def libdmtx_function(fname, restype, *args):
"""Returns a foreign function exported by `libdmtx`.
Args:
fname (:obj:`str`): Name of the exported function as string.
restype (:obj:): Return type - one of the `ctypes` primitive C data
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*args: Arguments - a sequence of `ctypes` primitive C data types.
Returns:
cddl.CFunctionType: A wrapper around the function.
"""
prototype = CFUNCTYPE(restype, *args)
return prototype((fname, load_libdmtx())) | python | def libdmtx_function(fname, restype, *args):
prototype = CFUNCTYPE(restype, *args)
return prototype((fname, load_libdmtx())) | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/wrapper.py#L46-L59 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/dmtx_library.py | load | def load():
"""Loads the libdmtx shared library.
"""
if 'Windows' == platform.system():
# Possible scenarios here
# 1. Run from source, DLLs are in pylibdmtx directory
# cdll.LoadLibrary() imports DLLs in repo root directory
# 2. Wheel install into CPython installation
# cdll.LoadLibrary() imports DLLs in package directory
# 3. Wheel install into virtualenv
# cdll.LoadLibrary() imports DLLs in package directory
# 4. Frozen
# cdll.LoadLibrary() imports DLLs alongside executable
fname = _windows_fname()
try:
libdmtx = cdll.LoadLibrary(fname)
except OSError:
libdmtx = cdll.LoadLibrary(
str(Path(__file__).parent.joinpath(fname))
)
else:
# Assume a shared library on the path
path = find_library('dmtx')
if not path:
raise ImportError('Unable to find dmtx shared library')
libdmtx = cdll.LoadLibrary(path)
return libdmtx | python | def load():
if 'Windows' == platform.system():
fname = _windows_fname()
try:
libdmtx = cdll.LoadLibrary(fname)
except OSError:
libdmtx = cdll.LoadLibrary(
str(Path(__file__).parent.joinpath(fname))
)
else:
path = find_library('dmtx')
if not path:
raise ImportError('Unable to find dmtx shared library')
libdmtx = cdll.LoadLibrary(path)
return libdmtx | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/dmtx_library.py#L22-L50 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/pylibdmtx.py | _image | def _image(pixels, width, height, pack):
"""A context manager for `DmtxImage`, created and destroyed by
`dmtxImageCreate` and `dmtxImageDestroy`.
Args:
pixels (:obj:):
width (int):
height (int):
pack (int):
Yields:
DmtxImage: The created image
Raises:
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image = dmtxImageCreate(pixels, width, height, pack)
if not image:
raise PyLibDMTXError('Could not create image')
else:
try:
yield image
finally:
dmtxImageDestroy(byref(image)) | python | def _image(pixels, width, height, pack):
image = dmtxImageCreate(pixels, width, height, pack)
if not image:
raise PyLibDMTXError('Could not create image')
else:
try:
yield image
finally:
dmtxImageDestroy(byref(image)) | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/pylibdmtx.py#L57-L80 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/pylibdmtx.py | _decoder | def _decoder(image, shrink):
"""A context manager for `DmtxDecode`, created and destroyed by
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image (POINTER(DmtxImage)):
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finally:
dmtxDecodeDestroy(byref(decoder)) | python | def _decoder(image, shrink):
decoder = dmtxDecodeCreate(image, shrink)
if not decoder:
raise PyLibDMTXError('Could not create decoder')
else:
try:
yield decoder
finally:
dmtxDecodeDestroy(byref(decoder)) | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/pylibdmtx.py#L84-L105 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/pylibdmtx.py | _region | def _region(decoder, timeout):
"""A context manager for `DmtxRegion`, created and destroyed by
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Args:
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dmtxRegionDestroy(byref(region)) | python | def _region(decoder, timeout):
region = dmtxRegionFindNext(decoder, timeout)
try:
yield region
finally:
if region:
dmtxRegionDestroy(byref(region)) | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/pylibdmtx.py#L109-L125 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/pylibdmtx.py | _decoded_matrix_region | def _decoded_matrix_region(decoder, region, corrections):
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Args:
decoder (POINTER(DmtxDecode)):
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dmtxMessageDestroy(byref(message)) | python | def _decoded_matrix_region(decoder, region, corrections):
message = dmtxDecodeMatrixRegion(decoder, region, corrections)
try:
yield message
finally:
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dmtxMessageDestroy(byref(message)) | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/pylibdmtx.py#L129-L146 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/pylibdmtx.py | _decode_region | def _decode_region(decoder, region, corrections, shrink):
"""Decodes and returns the value in a region.
Args:
region (DmtxRegion):
Yields:
Decoded or None: The decoded value.
"""
with _decoded_matrix_region(decoder, region, corrections) as msg:
if msg:
# Coordinates
p00 = DmtxVector2()
p11 = DmtxVector2(1.0, 1.0)
dmtxMatrix3VMultiplyBy(
p00,
region.contents.fit2raw
)
dmtxMatrix3VMultiplyBy(p11, region.contents.fit2raw)
x0 = int((shrink * p00.X) + 0.5)
y0 = int((shrink * p00.Y) + 0.5)
x1 = int((shrink * p11.X) + 0.5)
y1 = int((shrink * p11.Y) + 0.5)
return Decoded(
string_at(msg.contents.output),
Rect(x0, y0, x1 - x0, y1 - y0)
)
else:
return None | python | def _decode_region(decoder, region, corrections, shrink):
with _decoded_matrix_region(decoder, region, corrections) as msg:
if msg:
p00 = DmtxVector2()
p11 = DmtxVector2(1.0, 1.0)
dmtxMatrix3VMultiplyBy(
p00,
region.contents.fit2raw
)
dmtxMatrix3VMultiplyBy(p11, region.contents.fit2raw)
x0 = int((shrink * p00.X) + 0.5)
y0 = int((shrink * p00.Y) + 0.5)
x1 = int((shrink * p11.X) + 0.5)
y1 = int((shrink * p11.Y) + 0.5)
return Decoded(
string_at(msg.contents.output),
Rect(x0, y0, x1 - x0, y1 - y0)
)
else:
return None | [
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Args:
region (DmtxRegion):
Yields:
Decoded or None: The decoded value. | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/pylibdmtx.py#L149-L177 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/pylibdmtx.py | _pixel_data | def _pixel_data(image):
"""Returns (pixels, width, height, bpp)
Returns:
:obj: `tuple` (pixels, width, height, bpp)
"""
# Test for PIL.Image and numpy.ndarray without requiring that cv2 or PIL
# are installed.
if 'PIL.' in str(type(image)):
pixels = image.tobytes()
width, height = image.size
elif 'numpy.ndarray' in str(type(image)):
if 'uint8' != str(image.dtype):
image = image.astype('uint8')
try:
pixels = image.tobytes()
except AttributeError:
# `numpy.ndarray.tobytes()` introduced in `numpy` 1.9.0 - use the
# older `tostring` method.
pixels = image.tostring()
height, width = image.shape[:2]
else:
# image should be a tuple (pixels, width, height)
pixels, width, height = image
# Check dimensions
if 0 != len(pixels) % (width * height):
raise PyLibDMTXError(
(
'Inconsistent dimensions: image data of {0} bytes is not '
'divisible by (width x height = {1})'
).format(len(pixels), (width * height))
)
# Compute bits-per-pixel
bpp = 8 * len(pixels) // (width * height)
if bpp not in _PACK_ORDER:
raise PyLibDMTXError(
'Unsupported bits-per-pixel: [{0}] Should be one of {1}'.format(
bpp, sorted(_PACK_ORDER.keys())
)
)
return pixels, width, height, bpp | python | def _pixel_data(image):
if 'PIL.' in str(type(image)):
pixels = image.tobytes()
width, height = image.size
elif 'numpy.ndarray' in str(type(image)):
if 'uint8' != str(image.dtype):
image = image.astype('uint8')
try:
pixels = image.tobytes()
except AttributeError:
pixels = image.tostring()
height, width = image.shape[:2]
else:
pixels, width, height = image
if 0 != len(pixels) % (width * height):
raise PyLibDMTXError(
(
'Inconsistent dimensions: image data of {0} bytes is not '
'divisible by (width x height = {1})'
).format(len(pixels), (width * height))
)
bpp = 8 * len(pixels) // (width * height)
if bpp not in _PACK_ORDER:
raise PyLibDMTXError(
'Unsupported bits-per-pixel: [{0}] Should be one of {1}'.format(
bpp, sorted(_PACK_ORDER.keys())
)
)
return pixels, width, height, bpp | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/pylibdmtx.py#L180-L223 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/pylibdmtx.py | decode | def decode(image, timeout=None, gap_size=None, shrink=1, shape=None,
deviation=None, threshold=None, min_edge=None, max_edge=None,
corrections=None, max_count=None):
"""Decodes datamatrix barcodes in `image`.
Args:
image: `numpy.ndarray`, `PIL.Image` or tuple (pixels, width, height)
timeout (int): milliseconds
gap_size (int):
shrink (int):
shape (int):
deviation (int):
threshold (int):
min_edge (int):
max_edge (int):
corrections (int):
max_count (int): stop after reading this many barcodes. `None` to read
as many as possible.
Returns:
:obj:`list` of :obj:`Decoded`: The values decoded from barcodes.
"""
dmtx_timeout = None
if timeout:
now = dmtxTimeNow()
dmtx_timeout = dmtxTimeAdd(now, timeout)
if max_count is not None and max_count < 1:
raise ValueError('Invalid max_count [{0}]'.format(max_count))
pixels, width, height, bpp = _pixel_data(image)
results = []
with _image(
cast(pixels, c_ubyte_p), width, height, _PACK_ORDER[bpp]
) as img:
with _decoder(img, shrink) as decoder:
properties = [
(DmtxProperty.DmtxPropScanGap, gap_size),
(DmtxProperty.DmtxPropSymbolSize, shape),
(DmtxProperty.DmtxPropSquareDevn, deviation),
(DmtxProperty.DmtxPropEdgeThresh, threshold),
(DmtxProperty.DmtxPropEdgeMin, min_edge),
(DmtxProperty.DmtxPropEdgeMax, max_edge)
]
# Set only those properties with a non-None value
for prop, value in ((p, v) for p, v in properties if v is not None):
dmtxDecodeSetProp(decoder, prop, value)
if not corrections:
corrections = DmtxUndefined
while True:
with _region(decoder, dmtx_timeout) as region:
# Finished file or ran out of time before finding another
# region
if not region:
break
else:
# Decoded
res = _decode_region(
decoder, region, corrections, shrink
)
if res:
results.append(res)
# Stop if we've reached maximum count
if max_count and len(results) == max_count:
break
return results | python | def decode(image, timeout=None, gap_size=None, shrink=1, shape=None,
deviation=None, threshold=None, min_edge=None, max_edge=None,
corrections=None, max_count=None):
dmtx_timeout = None
if timeout:
now = dmtxTimeNow()
dmtx_timeout = dmtxTimeAdd(now, timeout)
if max_count is not None and max_count < 1:
raise ValueError('Invalid max_count [{0}]'.format(max_count))
pixels, width, height, bpp = _pixel_data(image)
results = []
with _image(
cast(pixels, c_ubyte_p), width, height, _PACK_ORDER[bpp]
) as img:
with _decoder(img, shrink) as decoder:
properties = [
(DmtxProperty.DmtxPropScanGap, gap_size),
(DmtxProperty.DmtxPropSymbolSize, shape),
(DmtxProperty.DmtxPropSquareDevn, deviation),
(DmtxProperty.DmtxPropEdgeThresh, threshold),
(DmtxProperty.DmtxPropEdgeMin, min_edge),
(DmtxProperty.DmtxPropEdgeMax, max_edge)
]
for prop, value in ((p, v) for p, v in properties if v is not None):
dmtxDecodeSetProp(decoder, prop, value)
if not corrections:
corrections = DmtxUndefined
while True:
with _region(decoder, dmtx_timeout) as region:
if not region:
break
else:
res = _decode_region(
decoder, region, corrections, shrink
)
if res:
results.append(res)
if max_count and len(results) == max_count:
break
return results | [
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Args:
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timeout (int): milliseconds
gap_size (int):
shrink (int):
shape (int):
deviation (int):
threshold (int):
min_edge (int):
max_edge (int):
corrections (int):
max_count (int): stop after reading this many barcodes. `None` to read
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/pylibdmtx.py#L226-L297 |
NaturalHistoryMuseum/pylibdmtx | pylibdmtx/pylibdmtx.py | encode | def encode(data, scheme=None, size=None):
"""
Encodes `data` in a DataMatrix image.
For now bpp is the libdmtx default which is 24
Args:
data: bytes instance
scheme: encoding scheme - one of `ENCODING_SCHEME_NAMES`, or `None`.
If `None`, defaults to 'Ascii'.
size: image dimensions - one of `ENCODING_SIZE_NAMES`, or `None`.
If `None`, defaults to 'ShapeAuto'.
Returns:
Encoded: with properties `(width, height, bpp, pixels)`.
You can use that result to build a PIL image:
Image.frombytes('RGB', (width, height), pixels)
"""
size = size if size else 'ShapeAuto'
size_name = '{0}{1}'.format(ENCODING_SIZE_PREFIX, size)
if not hasattr(DmtxSymbolSize, size_name):
raise PyLibDMTXError(
'Invalid size [{0}]: should be one of {1}'.format(
size, ENCODING_SIZE_NAMES
)
)
size = getattr(DmtxSymbolSize, size_name)
scheme = scheme if scheme else 'Ascii'
scheme_name = '{0}{1}'.format(
ENCODING_SCHEME_PREFIX, scheme.capitalize()
)
if not hasattr(DmtxScheme, scheme_name):
raise PyLibDMTXError(
'Invalid scheme [{0}]: should be one of {1}'.format(
scheme, ENCODING_SCHEME_NAMES
)
)
scheme = getattr(DmtxScheme, scheme_name)
with _encoder() as encoder:
dmtxEncodeSetProp(encoder, DmtxProperty.DmtxPropScheme, scheme)
dmtxEncodeSetProp(encoder, DmtxProperty.DmtxPropSizeRequest, size)
if dmtxEncodeDataMatrix(encoder, len(data), cast(data, c_ubyte_p)) == 0:
raise PyLibDMTXError(
'Could not encode data, possibly because the image is not '
'large enough to contain the data'
)
w, h, bpp = map(
partial(dmtxImageGetProp, encoder[0].image),
(
DmtxProperty.DmtxPropWidth, DmtxProperty.DmtxPropHeight,
DmtxProperty.DmtxPropBitsPerPixel
)
)
size = w * h * bpp // 8
pixels = cast(
encoder[0].image[0].pxl, ctypes.POINTER(ctypes.c_ubyte * size)
)
return Encoded(
width=w, height=h, bpp=bpp, pixels=ctypes.string_at(pixels, size)
) | python | def encode(data, scheme=None, size=None):
size = size if size else 'ShapeAuto'
size_name = '{0}{1}'.format(ENCODING_SIZE_PREFIX, size)
if not hasattr(DmtxSymbolSize, size_name):
raise PyLibDMTXError(
'Invalid size [{0}]: should be one of {1}'.format(
size, ENCODING_SIZE_NAMES
)
)
size = getattr(DmtxSymbolSize, size_name)
scheme = scheme if scheme else 'Ascii'
scheme_name = '{0}{1}'.format(
ENCODING_SCHEME_PREFIX, scheme.capitalize()
)
if not hasattr(DmtxScheme, scheme_name):
raise PyLibDMTXError(
'Invalid scheme [{0}]: should be one of {1}'.format(
scheme, ENCODING_SCHEME_NAMES
)
)
scheme = getattr(DmtxScheme, scheme_name)
with _encoder() as encoder:
dmtxEncodeSetProp(encoder, DmtxProperty.DmtxPropScheme, scheme)
dmtxEncodeSetProp(encoder, DmtxProperty.DmtxPropSizeRequest, size)
if dmtxEncodeDataMatrix(encoder, len(data), cast(data, c_ubyte_p)) == 0:
raise PyLibDMTXError(
'Could not encode data, possibly because the image is not '
'large enough to contain the data'
)
w, h, bpp = map(
partial(dmtxImageGetProp, encoder[0].image),
(
DmtxProperty.DmtxPropWidth, DmtxProperty.DmtxPropHeight,
DmtxProperty.DmtxPropBitsPerPixel
)
)
size = w * h * bpp // 8
pixels = cast(
encoder[0].image[0].pxl, ctypes.POINTER(ctypes.c_ubyte * size)
)
return Encoded(
width=w, height=h, bpp=bpp, pixels=ctypes.string_at(pixels, size)
) | [
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| Encodes `data` in a DataMatrix image.
For now bpp is the libdmtx default which is 24
Args:
data: bytes instance
scheme: encoding scheme - one of `ENCODING_SCHEME_NAMES`, or `None`.
If `None`, defaults to 'Ascii'.
size: image dimensions - one of `ENCODING_SIZE_NAMES`, or `None`.
If `None`, defaults to 'ShapeAuto'.
Returns:
Encoded: with properties `(width, height, bpp, pixels)`.
You can use that result to build a PIL image:
Image.frombytes('RGB', (width, height), pixels) | [
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| train | https://github.com/NaturalHistoryMuseum/pylibdmtx/blob/a425ec36050500af4875bf94eda02feb26ea62ad/pylibdmtx/pylibdmtx.py#L312-L378 |
GeoPyTool/GeoPyTool | geopytool/Stereo.py | Stereo.lines | def lines(self, Width=1, Color='k'):
'''
read the Excel, then draw the wulf net and Plot points, job done~
'''
self.axes.clear()
# self.axes.set_xlim(-90, 450)
self.axes.set_ylim(0, 90)
titles = list('NWSE')
titles = ['N', '330', '300', 'W', '240', '210', 'S', '150', '120', 'E', '60', '30']
self.n = len(titles)
self.angles = np.arange(90, 90 + 360, 360.0 / self.n)
self.angles = np.array([90., 120., 150., 180., 210., 240., 270., 300., 330.,
360., 30., 60.])
self.axes.set_thetagrids(self.angles, labels=titles, fontsize=14)
raw = self._df
Data = []
Labels = []
if (int(self.type_slider.value()) == 0):
list1 = [self.eqan(x) for x in range(15, 90, 15)]
else:
list1 = [self.eqar(x) for x in range(15, 90, 15)]
list2 = [str(x) for x in range(15, 90, 15)]
self.axes.set_rgrids(list1, list2)
for i in range(len(raw)):
Data.append([raw.at[i, 'Dip'], raw.at[i, 'Dip-Angle'], raw.at[i, 'Color'],
raw.at[i, 'Width'], raw.at[i, 'Alpha'], raw.at[i, 'Label']])
Dip = raw.at[i, 'Dip']
Dip_Angle = raw.at[i, 'Dip-Angle']
Label = raw.at[i, 'Label']
if (Label not in Labels):
Labels.append(Label)
else:
Label = ''
Width = 1
Color = 'red'
Alpha = 0.8
Marker = 'o'
Size = 50
Setting = [Width, Color, Alpha, Marker, Size]
Width = raw.at[i, 'Width']
Color = raw.at[i, 'Color']
Alpha = raw.at[i, 'Alpha']
Marker = raw.at[i, 'Marker']
Size = raw.at[i, 'Size']
if (Color not in Setting or Color != ''):
Width = raw.at[i, 'Width']
Color = raw.at[i, 'Color']
Alpha = raw.at[i, 'Alpha']
Marker = raw.at[i, 'Marker']
Size = raw.at[i, 'Size']
Setting = [Width, Color, Alpha, Marker, Size]
r = np.arange(Dip - 90, Dip + 91, 1)
BearR = [np.radians(-A + 90) for A in r]
if (int(self.type_slider.value()) == 0):
Line = (self.eqan(self.getangular(Dip_Angle, Dip, r)))
else:
Line = (self.eqar(self.getangular(Dip_Angle, Dip, r)))
self.axes.plot(BearR, Line, color=Color, linewidth=Width, alpha=Alpha, label=Label)
# self.axes.thetagrids(range(360 + 90, 0 + 90, -30), [str(x) for x in range(0, 360, 30)])
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.5, 1), loc=2, borderaxespad=0, prop=fontprop) | python | def lines(self, Width=1, Color='k'):
self.axes.clear()
self.axes.set_ylim(0, 90)
titles = list('NWSE')
titles = ['N', '330', '300', 'W', '240', '210', 'S', '150', '120', 'E', '60', '30']
self.n = len(titles)
self.angles = np.arange(90, 90 + 360, 360.0 / self.n)
self.angles = np.array([90., 120., 150., 180., 210., 240., 270., 300., 330.,
360., 30., 60.])
self.axes.set_thetagrids(self.angles, labels=titles, fontsize=14)
raw = self._df
Data = []
Labels = []
if (int(self.type_slider.value()) == 0):
list1 = [self.eqan(x) for x in range(15, 90, 15)]
else:
list1 = [self.eqar(x) for x in range(15, 90, 15)]
list2 = [str(x) for x in range(15, 90, 15)]
self.axes.set_rgrids(list1, list2)
for i in range(len(raw)):
Data.append([raw.at[i, 'Dip'], raw.at[i, 'Dip-Angle'], raw.at[i, 'Color'],
raw.at[i, 'Width'], raw.at[i, 'Alpha'], raw.at[i, 'Label']])
Dip = raw.at[i, 'Dip']
Dip_Angle = raw.at[i, 'Dip-Angle']
Label = raw.at[i, 'Label']
if (Label not in Labels):
Labels.append(Label)
else:
Label = ''
Width = 1
Color = 'red'
Alpha = 0.8
Marker = 'o'
Size = 50
Setting = [Width, Color, Alpha, Marker, Size]
Width = raw.at[i, 'Width']
Color = raw.at[i, 'Color']
Alpha = raw.at[i, 'Alpha']
Marker = raw.at[i, 'Marker']
Size = raw.at[i, 'Size']
if (Color not in Setting or Color != ''):
Width = raw.at[i, 'Width']
Color = raw.at[i, 'Color']
Alpha = raw.at[i, 'Alpha']
Marker = raw.at[i, 'Marker']
Size = raw.at[i, 'Size']
Setting = [Width, Color, Alpha, Marker, Size]
r = np.arange(Dip - 90, Dip + 91, 1)
BearR = [np.radians(-A + 90) for A in r]
if (int(self.type_slider.value()) == 0):
Line = (self.eqan(self.getangular(Dip_Angle, Dip, r)))
else:
Line = (self.eqar(self.getangular(Dip_Angle, Dip, r)))
self.axes.plot(BearR, Line, color=Color, linewidth=Width, alpha=Alpha, label=Label)
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.5, 1), loc=2, borderaxespad=0, prop=fontprop) | [
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| read the Excel, then draw the wulf net and Plot points, job done~ | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/Stereo.py#L142-L222 |
GeoPyTool/GeoPyTool | Experimental/Alpah_Shape_2D.py | add_edge | def add_edge(edges, edge_points, coords, i, j):
"""
Add a line between the i-th and j-th points,
if not in the list already
"""
if (i, j) in edges or (j, i) in edges:
# already added
return( edges.add((i, j)), edge_points.append(coords[[i, j]])) | python | def add_edge(edges, edge_points, coords, i, j):
if (i, j) in edges or (j, i) in edges:
return( edges.add((i, j)), edge_points.append(coords[[i, j]])) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/Experimental/Alpah_Shape_2D.py#L28-L35 |
GeoPyTool/GeoPyTool | Experimental/Alpah_Shape_2D.py | alpha_shape | def alpha_shape(points, alpha):
"""
Compute the alpha shape (concave hull) of a set
of points.
@param points: Iterable container of points.
@param alpha: alpha value to influence the
gooeyness of the border. Smaller numbers
don't fall inward as much as larger numbers.
Too large, and you lose everything!
"""
if len(points) < 4:
# When you have a triangle, there is no sense
# in computing an alpha shape.
return geometry.MultiPoint(list(points)).convex_hull
#coords = np.array([point.coords[0] for point in points])
coords = np.array(points)
print(coords)
tri = Delaunay(coords)
edges = set()
edge_points = []
# loop over triangles:
# ia, ib, ic = indices of corner points of the
# triangle
for ia, ib, ic in tri.vertices:
pa = coords[ia]
pb = coords[ib]
pc = coords[ic]
# Lengths of sides of triangle
a = math.sqrt((pa[0]-pb[0])**2 + (pa[1]-pb[1])**2)
b = math.sqrt((pb[0]-pc[0])**2 + (pb[1]-pc[1])**2)
c = math.sqrt((pc[0]-pa[0])**2 + (pc[1]-pa[1])**2)
# Semiperimeter of triangle
s = (a + b + c)/2.0
# Area of triangle by Heron's formula
area = math.sqrt(s*(s-a)*(s-b)*(s-c))
circum_r = a*b*c/(4.0*area)
# Here's the radius filter.
#print circum_r
if circum_r < 1.0/alpha:
add_edge(edges, edge_points, coords, ia, ib)
add_edge(edges, edge_points, coords, ib, ic)
add_edge(edges, edge_points, coords, ic, ia)
m = geometry.MultiLineString(edge_points)
triangles = list(polygonize(m))
return (cascaded_union(triangles), edge_points)
print (cascaded_union(triangles), edge_points) | python | def alpha_shape(points, alpha):
if len(points) < 4:
return geometry.MultiPoint(list(points)).convex_hull
coords = np.array(points)
print(coords)
tri = Delaunay(coords)
edges = set()
edge_points = []
for ia, ib, ic in tri.vertices:
pa = coords[ia]
pb = coords[ib]
pc = coords[ic]
a = math.sqrt((pa[0]-pb[0])**2 + (pa[1]-pb[1])**2)
b = math.sqrt((pb[0]-pc[0])**2 + (pb[1]-pc[1])**2)
c = math.sqrt((pc[0]-pa[0])**2 + (pc[1]-pa[1])**2)
s = (a + b + c)/2.0
area = math.sqrt(s*(s-a)*(s-b)*(s-c))
circum_r = a*b*c/(4.0*area)
if circum_r < 1.0/alpha:
add_edge(edges, edge_points, coords, ia, ib)
add_edge(edges, edge_points, coords, ib, ic)
add_edge(edges, edge_points, coords, ic, ia)
m = geometry.MultiLineString(edge_points)
triangles = list(polygonize(m))
return (cascaded_union(triangles), edge_points)
print (cascaded_union(triangles), edge_points) | [
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| Compute the alpha shape (concave hull) of a set
of points.
@param points: Iterable container of points.
@param alpha: alpha value to influence the
gooeyness of the border. Smaller numbers
don't fall inward as much as larger numbers.
Too large, and you lose everything! | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/Experimental/Alpah_Shape_2D.py#L39-L89 |
GeoPyTool/GeoPyTool | geopytool/MyFA - Copy.py | MyFA.Distance_Calculation | def Distance_Calculation(self):
print(self.whole_labels)
distance_result={}
#distance_result[self.whole_labels[i]] = []
print(distance_result)
for i in range(len(self.whole_labels)):
#print(self.whole_labels[i], self.fa_result[self.result_to_fit.index == self.whole_labels[i]][0])
print( self.whole_labels[i], len(self.fa_result[self.result_to_fit.index == self.whole_labels[i]]))
pass
'''
for i in range(len(self.whole_labels)):
for j in range(len(self.whole_labels)):
if i ==j:
pass
else:
distance_result[self.whole_labels[i] + ' to ' + self.whole_labels[j]] = []
self.fa_result[self.result_to_fit.index == self.whole_labels[i]]
self.fa_result[self.result_to_fit.index == self.whole_labels[j]]
for m in range(len(self.fa_result[self.result_to_fit.index == self.whole_labels[i]])):
for n in range(len(self.fa_result[self.result_to_fit.index == self.whole_labels[j]])):
pass
self.fa_result[self.result_to_fit.index == self.whole_labels[i]][m]
#tmp_dist= self.Hsim_Distance(self.fa_result[self.result_to_fit.index == self.whole_labels[i]][m],self.fa_result[self.result_to_fit.index == self.whole_labels[j]][n])
#print(tmp_dist)
#distance_result[self.whole_labels[i] + ' to ' + self.whole_labels[j]].append(tmp_dist)
pass
'''
#print(self.fa_result)
try:
self.fa_data_to_test[self.data_to_test_to_fit.index == self.whole_labels[0], 0]
except Exception as e:
pass | python | def Distance_Calculation(self):
print(self.whole_labels)
distance_result={}
print(distance_result)
for i in range(len(self.whole_labels)):
print( self.whole_labels[i], len(self.fa_result[self.result_to_fit.index == self.whole_labels[i]]))
pass
try:
self.fa_data_to_test[self.data_to_test_to_fit.index == self.whole_labels[0], 0]
except Exception as e:
pass | [
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for j in range(len(self.whole_labels)):
if i ==j:
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else:
distance_result[self.whole_labels[i] + ' to ' + self.whole_labels[j]] = []
self.fa_result[self.result_to_fit.index == self.whole_labels[i]]
self.fa_result[self.result_to_fit.index == self.whole_labels[j]]
for m in range(len(self.fa_result[self.result_to_fit.index == self.whole_labels[i]])):
for n in range(len(self.fa_result[self.result_to_fit.index == self.whole_labels[j]])):
pass
self.fa_result[self.result_to_fit.index == self.whole_labels[i]][m]
#tmp_dist= self.Hsim_Distance(self.fa_result[self.result_to_fit.index == self.whole_labels[i]][m],self.fa_result[self.result_to_fit.index == self.whole_labels[j]][n])
#print(tmp_dist)
#distance_result[self.whole_labels[i] + ' to ' + self.whole_labels[j]].append(tmp_dist)
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/MyFA - Copy.py#L348-L395 |
GeoPyTool/GeoPyTool | geopytool/GLMultiDimension.py | GLMultiDimension.create_main_frame | def create_main_frame(self):
self.main_frame = QWidget()
#self.main_frame.setFixedSize(self.width(), self.width())
self.dpi = 128
self.ShapeGroups =200
self.view = gl.GLViewWidget()
#self.view = pg.PlotWidget()
#self.view.setFixedSize(self.width(),self.height())
self.view.setFixedSize(self.width(), self.width())
self.view.setParent(self.main_frame)
# Other GUI controls
self.save_button = QPushButton('&Save')
self.save_button.clicked.connect(self.saveImgFile)
self.draw_button = QPushButton('&Reset')
self.draw_button.clicked.connect(self.Reset)
self.load_button = QPushButton('&Load')
#self.load_button.clicked.connect(self.Load)
self.fit_cb= QCheckBox('&PolyFit')
self.fit_cb.setChecked(False)
self.fit_cb.stateChanged.connect(self.Magic) # int
self.fit_label = QLabel('Exp')
self.fit_seter = QLineEdit(self)
self.fit_seter.textChanged[str].connect(self.FitChanged)
self.shape_cb= QCheckBox('&Shape')
self.shape_cb.setChecked(False)
self.shape_cb.stateChanged.connect(self.Magic) # int
self.Normalize_cb = QCheckBox('&Normalize')
self.Normalize_cb.setChecked(False)
self.Normalize_cb.stateChanged.connect(self.Magic) # int
self.norm_slider_label = QLabel('Standard:' + self.NameChosen)
self.norm_slider = QSlider(Qt.Horizontal)
self.norm_slider.setRange(0, 4)
self.norm_slider.setValue(0)
self.norm_slider.setTracking(True)
self.norm_slider.setTickPosition(QSlider.TicksBothSides)
self.norm_slider.valueChanged.connect(self.Magic) # int
self.x_element = QSlider(Qt.Horizontal)
self.x_element.setRange(0, len(self.items) - 1)
self.x_element.setValue(0)
self.x_element.setTracking(True)
self.x_element.setTickPosition(QSlider.TicksBothSides)
self.x_element.valueChanged.connect(self.Magic) # int
self.x_element_label = QLabel('X')
self.logx_cb = QCheckBox('&Log')
self.logx_cb.setChecked(False)
self.logx_cb.stateChanged.connect(self.Magic) # int
self.y_element = QSlider(Qt.Horizontal)
self.y_element.setRange(0, len(self.items) - 1)
self.y_element.setValue(1)
self.y_element.setTracking(True)
self.y_element.setTickPosition(QSlider.TicksBothSides)
self.y_element.valueChanged.connect(self.Magic) # int
self.y_element_label = QLabel('Y')
self.logy_cb = QCheckBox('&Log')
self.logy_cb.setChecked(False)
self.logy_cb.stateChanged.connect(self.Magic) # int
self.z_element = QSlider(Qt.Horizontal)
self.z_element.setRange(0, len(self.items) - 1)
self.z_element.setValue(2)
self.z_element.setTracking(True)
self.z_element.setTickPosition(QSlider.TicksBothSides)
self.z_element.valueChanged.connect(self.Magic) # int
self.z_element_label = QLabel('Z')
self.logz_cb = QCheckBox('&Log')
self.logz_cb.setChecked(False)
self.logz_cb.stateChanged.connect(self.Magic) # int
self.xlim_seter_left_label = QLabel('Xleft')
self.xlim_seter_left = QLineEdit(self)
self.xlim_seter_left.textChanged[str].connect(self.XleftChanged)
self.xlim_seter_right_label = QLabel('Xright')
self.xlim_seter_right = QLineEdit(self)
self.xlim_seter_right.textChanged[str].connect(self.XrightChanged)
self.ylim_seter_down_label = QLabel('Ydown')
self.ylim_seter_down = QLineEdit(self)
self.ylim_seter_down.textChanged[str].connect(self.YdownChanged)
self.ylim_seter_up_label = QLabel('Yup')
self.ylim_seter_up = QLineEdit(self)
self.ylim_seter_up.textChanged[str].connect(self.YupChanged)
self.hbox0 = QHBoxLayout()
self.hbox1 = QHBoxLayout()
self.hbox2 = QHBoxLayout()
self.hbox3 = QHBoxLayout()
self.hbox4 = QHBoxLayout()
self.hbox5 = QHBoxLayout()
self.hbox6 = QHBoxLayout()
self.hbox7 = QHBoxLayout()
'''
for w in [self.fit_cb,self.fit_label, self.fit_seter,self.xlim_seter_left_label,self.xlim_seter_left,self.xlim_seter_right_label,self.xlim_seter_right,self.ylim_seter_down_label,self.ylim_seter_down,self.ylim_seter_up_label,self.ylim_seter_up,self.shape_cb]:
self.hbox0.addWidget(w)
self.hbox0.setAlignment(w, Qt.AlignVCenter)
'''
for w in [self.view]:
self.hbox0.addWidget(w)
self.hbox0.setAlignment(w, Qt.AlignVCenter)
for w in [self.Normalize_cb, self.norm_slider_label, self.norm_slider]:
self.hbox1.addWidget(w)
self.hbox1.setAlignment(w, Qt.AlignVCenter)
for w in [self.logx_cb, self.x_element_label, self.x_element]:
self.hbox2.addWidget(w)
self.hbox2.setAlignment(w, Qt.AlignVCenter)
for w in [self.logy_cb, self.y_element_label, self.y_element]:
self.hbox3.addWidget(w)
self.hbox3.setAlignment(w, Qt.AlignVCenter)
for w in [self.logz_cb, self.z_element_label, self.z_element]:
self.hbox4.addWidget(w)
self.hbox4.setAlignment(w, Qt.AlignVCenter)
self.vbox = QVBoxLayout()
#self.vbox.addWidget(self.view)
self.vbox.addLayout(self.hbox0)
self.vbox.addLayout(self.hbox1)
self.vbox.addLayout(self.hbox2)
self.vbox.addLayout(self.hbox3)
self.vbox.addLayout(self.hbox4)
self.main_frame.setLayout(self.vbox)
self.setCentralWidget(self.main_frame) | python | def create_main_frame(self):
self.main_frame = QWidget()
self.dpi = 128
self.ShapeGroups =200
self.view = gl.GLViewWidget()
self.view.setFixedSize(self.width(), self.width())
self.view.setParent(self.main_frame)
self.save_button = QPushButton('&Save')
self.save_button.clicked.connect(self.saveImgFile)
self.draw_button = QPushButton('&Reset')
self.draw_button.clicked.connect(self.Reset)
self.load_button = QPushButton('&Load')
self.fit_cb= QCheckBox('&PolyFit')
self.fit_cb.setChecked(False)
self.fit_cb.stateChanged.connect(self.Magic)
self.fit_label = QLabel('Exp')
self.fit_seter = QLineEdit(self)
self.fit_seter.textChanged[str].connect(self.FitChanged)
self.shape_cb= QCheckBox('&Shape')
self.shape_cb.setChecked(False)
self.shape_cb.stateChanged.connect(self.Magic)
self.Normalize_cb = QCheckBox('&Normalize')
self.Normalize_cb.setChecked(False)
self.Normalize_cb.stateChanged.connect(self.Magic)
self.norm_slider_label = QLabel('Standard:' + self.NameChosen)
self.norm_slider = QSlider(Qt.Horizontal)
self.norm_slider.setRange(0, 4)
self.norm_slider.setValue(0)
self.norm_slider.setTracking(True)
self.norm_slider.setTickPosition(QSlider.TicksBothSides)
self.norm_slider.valueChanged.connect(self.Magic)
self.x_element = QSlider(Qt.Horizontal)
self.x_element.setRange(0, len(self.items) - 1)
self.x_element.setValue(0)
self.x_element.setTracking(True)
self.x_element.setTickPosition(QSlider.TicksBothSides)
self.x_element.valueChanged.connect(self.Magic)
self.x_element_label = QLabel('X')
self.logx_cb = QCheckBox('&Log')
self.logx_cb.setChecked(False)
self.logx_cb.stateChanged.connect(self.Magic)
self.y_element = QSlider(Qt.Horizontal)
self.y_element.setRange(0, len(self.items) - 1)
self.y_element.setValue(1)
self.y_element.setTracking(True)
self.y_element.setTickPosition(QSlider.TicksBothSides)
self.y_element.valueChanged.connect(self.Magic)
self.y_element_label = QLabel('Y')
self.logy_cb = QCheckBox('&Log')
self.logy_cb.setChecked(False)
self.logy_cb.stateChanged.connect(self.Magic)
self.z_element = QSlider(Qt.Horizontal)
self.z_element.setRange(0, len(self.items) - 1)
self.z_element.setValue(2)
self.z_element.setTracking(True)
self.z_element.setTickPosition(QSlider.TicksBothSides)
self.z_element.valueChanged.connect(self.Magic)
self.z_element_label = QLabel('Z')
self.logz_cb = QCheckBox('&Log')
self.logz_cb.setChecked(False)
self.logz_cb.stateChanged.connect(self.Magic)
self.xlim_seter_left_label = QLabel('Xleft')
self.xlim_seter_left = QLineEdit(self)
self.xlim_seter_left.textChanged[str].connect(self.XleftChanged)
self.xlim_seter_right_label = QLabel('Xright')
self.xlim_seter_right = QLineEdit(self)
self.xlim_seter_right.textChanged[str].connect(self.XrightChanged)
self.ylim_seter_down_label = QLabel('Ydown')
self.ylim_seter_down = QLineEdit(self)
self.ylim_seter_down.textChanged[str].connect(self.YdownChanged)
self.ylim_seter_up_label = QLabel('Yup')
self.ylim_seter_up = QLineEdit(self)
self.ylim_seter_up.textChanged[str].connect(self.YupChanged)
self.hbox0 = QHBoxLayout()
self.hbox1 = QHBoxLayout()
self.hbox2 = QHBoxLayout()
self.hbox3 = QHBoxLayout()
self.hbox4 = QHBoxLayout()
self.hbox5 = QHBoxLayout()
self.hbox6 = QHBoxLayout()
self.hbox7 = QHBoxLayout()
for w in [self.view]:
self.hbox0.addWidget(w)
self.hbox0.setAlignment(w, Qt.AlignVCenter)
for w in [self.Normalize_cb, self.norm_slider_label, self.norm_slider]:
self.hbox1.addWidget(w)
self.hbox1.setAlignment(w, Qt.AlignVCenter)
for w in [self.logx_cb, self.x_element_label, self.x_element]:
self.hbox2.addWidget(w)
self.hbox2.setAlignment(w, Qt.AlignVCenter)
for w in [self.logy_cb, self.y_element_label, self.y_element]:
self.hbox3.addWidget(w)
self.hbox3.setAlignment(w, Qt.AlignVCenter)
for w in [self.logz_cb, self.z_element_label, self.z_element]:
self.hbox4.addWidget(w)
self.hbox4.setAlignment(w, Qt.AlignVCenter)
self.vbox = QVBoxLayout()
self.vbox.addLayout(self.hbox0)
self.vbox.addLayout(self.hbox1)
self.vbox.addLayout(self.hbox2)
self.vbox.addLayout(self.hbox3)
self.vbox.addLayout(self.hbox4)
self.main_frame.setLayout(self.vbox)
self.setCentralWidget(self.main_frame) | [
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| for w in [self.fit_cb,self.fit_label, self.fit_seter,self.xlim_seter_left_label,self.xlim_seter_left,self.xlim_seter_right_label,self.xlim_seter_right,self.ylim_seter_down_label,self.ylim_seter_down,self.ylim_seter_up_label,self.ylim_seter_up,self.shape_cb]:
self.hbox0.addWidget(w)
self.hbox0.setAlignment(w, Qt.AlignVCenter) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/GLMultiDimension.py#L125-L291 |
GeoPyTool/GeoPyTool | geopytool/GLMultiDimension.py | GLMultiDimension.Magic | def Magic(self):
#self.view.setFixedSize(self.width(), self.width())
self.WholeData = []
self.x_scale = self.width_plot / self.width_load
self.y_scale = self.height_plot / self.height_load
self.z_scale = self.depth_plot / self.depth_load
# print(self.x_scale,' and ',self.x_scale)
raw = self._df
a = int(self.x_element.value())
b = int(self.y_element.value())
c = int(self.z_element.value())
self.x_element_label.setText(self.items[a])
self.y_element_label.setText(self.items[b])
self.z_element_label.setText(self.items[c])
if (self.Left != self.Right) and (self.Down != self.Up) and abs(self.Left) + abs(self.Right) + abs(
self.Down) + abs(self.Up) != 0:
self.extent = [self.Left, self.Right, self.Down, self.Up]
elif (self.Left == self.Right and abs(self.Left) + abs(self.Right) != 0):
reply = QMessageBox.warning(self, 'Warning', 'You set same value to Left and Right limits.')
self.extent = 0
elif (self.Down == self.Up and abs(self.Down) + abs(self.Up) != 0):
reply = QMessageBox.warning(self, 'Warning', 'You set same value to Up and Down limits.')
self.extent = 0
else:
self.extent = 0
standardnamechosen = self.StandardsName[int(self.norm_slider.value())]
standardchosen = self.Standards[standardnamechosen]
self.norm_slider_label.setText(standardnamechosen)
PointLabels = []
XtoDraw = []
YtoDraw = []
ZtoDraw = []
Colors=[]
Alphas=[]
Markers=[]
Names=[]
for i in range(len(raw)):
# raw.at[i, 'DataType'] == 'User' or raw.at[i, 'DataType'] == 'user' or raw.at[i, 'DataType'] == 'USER'
TmpLabel = ''
# self.WholeData.append(math.log(tmp, 10))
if (raw.at[i, 'Label'] in PointLabels or raw.at[i, 'Label'] == ''):
TmpLabel = ''
else:
PointLabels.append(raw.at[i, 'Label'])
TmpLabel = raw.at[i, 'Label']
x, y ,z = 0, 0, 0
xuse, yuse,zuse = 0, 0, 0
x, y,z = raw.at[i, self.items[a]], raw.at[i, self.items[b]],raw.at[i, self.items[c]]
try:
xuse = x
yuse = y
zuse = z
self.xlabel = self.items[a]
self.ylabel = self.items[b]
self.zlabel = self.items[c]
if (self.Normalize_cb.isChecked()):
self.xlabel = self.items[a] + ' Norm by ' + standardnamechosen
self.x_element_label.setText(self.xlabel)
self.ylabel = self.items[b] + ' Norm by ' + standardnamechosen
self.y_element_label.setText(self.ylabel)
self.zlabel = self.items[c] + ' Norm by ' + standardnamechosen
self.z_element_label.setText(self.zlabel)
if self.items[a] in self.Element:
xuse = xuse / standardchosen[self.items[a]]
if self.items[b] in self.Element:
yuse = yuse / standardchosen[self.items[b]]
if self.items[c] in self.Element:
zuse = zuse / standardchosen[self.items[c]]
if (self.logx_cb.isChecked()):
xuse = math.log(x, 10)
self.xlabel = '$log10$ ' + self.xlabel
if (self.logy_cb.isChecked()):
yuse = math.log(y, 10)
self.ylabel = '$log10$ ' + self.ylabel
if (self.logz_cb.isChecked()):
zuse = math.log(z, 10)
self.zlabel = '$log10$ ' + self.zlabel
XtoDraw.append(xuse)
YtoDraw.append(yuse)
ZtoDraw.append(zuse)
Colors.append(raw.at[i, 'Color'])
Alphas.append(raw.at[i, 'Alpha'])
Names.append(raw.at[i, 'Label'])
Markers.append(raw.at[i, 'Marker'])
except(ValueError):
pass
if self.LimSet==False:
self.Xleft, self.Xright, self.Ydown, self.Yup, self.Tail, self.Head = min(XtoDraw), max(XtoDraw), min(YtoDraw), max(YtoDraw), min(ZtoDraw), max(ZtoDraw)
xmin, xmax = min(XtoDraw), max(XtoDraw)
ymin, ymax = min(YtoDraw), max(YtoDraw)
zmin, zmax = min(ZtoDraw), max(ZtoDraw)
xmean = np.mean(XtoDraw)
ymean = np.mean(YtoDraw)
zmean = np.mean(ZtoDraw)
Xoriginal = np.arange(xmin, xmax, (xmax - xmin) / 10)
Yoriginal = np.arange(ymin, ymax, (ymax - ymin) / 10)
Zoriginal = np.arange(zmin, zmax, (zmax - zmin) / 10)
XonPlot = self.GetASequence(tail=self.ShapeGroups)
YonPlot = self.GetASequence(tail=self.ShapeGroups)
ZonPlot = self.GetASequence(tail=self.ShapeGroups)
XonStick = []
YonStick = []
ZonStick = []
for i in range(len(XonPlot)):
XonStick.append([XonPlot[i], Xoriginal[i]])
YonStick.append([YonPlot[i], Yoriginal[i]])
ZonStick.append([ZonPlot[i], Zoriginal[i]])
pass
#print(XtoDraw,'\n', YtoDraw,'\n', ZtoDraw)
toDf = {self.xlabel:XtoDraw,
self.ylabel:YtoDraw,
self.zlabel:ZtoDraw}
newdf = pd.DataFrame(toDf)
pos = newdf.as_matrix()
print(pos)
ThreeDimView = gl.GLScatterPlotItem(pos=pos, color=(100, 255, 255, 88), size=0.1, pxMode=False)
print(xmean,'\n', ymean,'\n', zmean,'\n')
self.view.pan(xmean, ymean, zmean)
xgrid = gl.GLGridItem(size=QtGui.QVector3D(10, 10, 1), color=1)
ygrid = gl.GLGridItem(size=QtGui.QVector3D(20, 20, 2), color=2)
zgrid = gl.GLGridItem(size=QtGui.QVector3D(30, 30, 3), color=3)
## rotate x and y grids to face the correct direction
xgrid.rotate(90, 0, 1, 0)
ygrid.rotate(90, 1, 0, 0)
xgrid.translate(xmean, ymean, zmean)
ygrid.translate(xmean, ymean, zmean)
zgrid.translate(xmean, ymean, zmean)
## scale each grid differently
'''
xgrid.scale(12.8, 12.8, 12.8)
ygrid.scale(12.8, 12.8, 12.8)
zgrid.scale(12.8, 12.8, 12.8)
'''
# xgrid.setTransform(xmean,ymean,zmean)
self.view.addItem(xgrid)
self.view.addItem(ygrid)
self.view.addItem(zgrid)
self.view.addItem(ThreeDimView) | python | def Magic(self):
self.WholeData = []
self.x_scale = self.width_plot / self.width_load
self.y_scale = self.height_plot / self.height_load
self.z_scale = self.depth_plot / self.depth_load
raw = self._df
a = int(self.x_element.value())
b = int(self.y_element.value())
c = int(self.z_element.value())
self.x_element_label.setText(self.items[a])
self.y_element_label.setText(self.items[b])
self.z_element_label.setText(self.items[c])
if (self.Left != self.Right) and (self.Down != self.Up) and abs(self.Left) + abs(self.Right) + abs(
self.Down) + abs(self.Up) != 0:
self.extent = [self.Left, self.Right, self.Down, self.Up]
elif (self.Left == self.Right and abs(self.Left) + abs(self.Right) != 0):
reply = QMessageBox.warning(self, 'Warning', 'You set same value to Left and Right limits.')
self.extent = 0
elif (self.Down == self.Up and abs(self.Down) + abs(self.Up) != 0):
reply = QMessageBox.warning(self, 'Warning', 'You set same value to Up and Down limits.')
self.extent = 0
else:
self.extent = 0
standardnamechosen = self.StandardsName[int(self.norm_slider.value())]
standardchosen = self.Standards[standardnamechosen]
self.norm_slider_label.setText(standardnamechosen)
PointLabels = []
XtoDraw = []
YtoDraw = []
ZtoDraw = []
Colors=[]
Alphas=[]
Markers=[]
Names=[]
for i in range(len(raw)):
TmpLabel = ''
if (raw.at[i, 'Label'] in PointLabels or raw.at[i, 'Label'] == ''):
TmpLabel = ''
else:
PointLabels.append(raw.at[i, 'Label'])
TmpLabel = raw.at[i, 'Label']
x, y ,z = 0, 0, 0
xuse, yuse,zuse = 0, 0, 0
x, y,z = raw.at[i, self.items[a]], raw.at[i, self.items[b]],raw.at[i, self.items[c]]
try:
xuse = x
yuse = y
zuse = z
self.xlabel = self.items[a]
self.ylabel = self.items[b]
self.zlabel = self.items[c]
if (self.Normalize_cb.isChecked()):
self.xlabel = self.items[a] + ' Norm by ' + standardnamechosen
self.x_element_label.setText(self.xlabel)
self.ylabel = self.items[b] + ' Norm by ' + standardnamechosen
self.y_element_label.setText(self.ylabel)
self.zlabel = self.items[c] + ' Norm by ' + standardnamechosen
self.z_element_label.setText(self.zlabel)
if self.items[a] in self.Element:
xuse = xuse / standardchosen[self.items[a]]
if self.items[b] in self.Element:
yuse = yuse / standardchosen[self.items[b]]
if self.items[c] in self.Element:
zuse = zuse / standardchosen[self.items[c]]
if (self.logx_cb.isChecked()):
xuse = math.log(x, 10)
self.xlabel = '$log10$ ' + self.xlabel
if (self.logy_cb.isChecked()):
yuse = math.log(y, 10)
self.ylabel = '$log10$ ' + self.ylabel
if (self.logz_cb.isChecked()):
zuse = math.log(z, 10)
self.zlabel = '$log10$ ' + self.zlabel
XtoDraw.append(xuse)
YtoDraw.append(yuse)
ZtoDraw.append(zuse)
Colors.append(raw.at[i, 'Color'])
Alphas.append(raw.at[i, 'Alpha'])
Names.append(raw.at[i, 'Label'])
Markers.append(raw.at[i, 'Marker'])
except(ValueError):
pass
if self.LimSet==False:
self.Xleft, self.Xright, self.Ydown, self.Yup, self.Tail, self.Head = min(XtoDraw), max(XtoDraw), min(YtoDraw), max(YtoDraw), min(ZtoDraw), max(ZtoDraw)
xmin, xmax = min(XtoDraw), max(XtoDraw)
ymin, ymax = min(YtoDraw), max(YtoDraw)
zmin, zmax = min(ZtoDraw), max(ZtoDraw)
xmean = np.mean(XtoDraw)
ymean = np.mean(YtoDraw)
zmean = np.mean(ZtoDraw)
Xoriginal = np.arange(xmin, xmax, (xmax - xmin) / 10)
Yoriginal = np.arange(ymin, ymax, (ymax - ymin) / 10)
Zoriginal = np.arange(zmin, zmax, (zmax - zmin) / 10)
XonPlot = self.GetASequence(tail=self.ShapeGroups)
YonPlot = self.GetASequence(tail=self.ShapeGroups)
ZonPlot = self.GetASequence(tail=self.ShapeGroups)
XonStick = []
YonStick = []
ZonStick = []
for i in range(len(XonPlot)):
XonStick.append([XonPlot[i], Xoriginal[i]])
YonStick.append([YonPlot[i], Yoriginal[i]])
ZonStick.append([ZonPlot[i], Zoriginal[i]])
pass
toDf = {self.xlabel:XtoDraw,
self.ylabel:YtoDraw,
self.zlabel:ZtoDraw}
newdf = pd.DataFrame(toDf)
pos = newdf.as_matrix()
print(pos)
ThreeDimView = gl.GLScatterPlotItem(pos=pos, color=(100, 255, 255, 88), size=0.1, pxMode=False)
print(xmean,'\n', ymean,'\n', zmean,'\n')
self.view.pan(xmean, ymean, zmean)
xgrid = gl.GLGridItem(size=QtGui.QVector3D(10, 10, 1), color=1)
ygrid = gl.GLGridItem(size=QtGui.QVector3D(20, 20, 2), color=2)
zgrid = gl.GLGridItem(size=QtGui.QVector3D(30, 30, 3), color=3)
xgrid.rotate(90, 0, 1, 0)
ygrid.rotate(90, 1, 0, 0)
xgrid.translate(xmean, ymean, zmean)
ygrid.translate(xmean, ymean, zmean)
zgrid.translate(xmean, ymean, zmean)
self.view.addItem(xgrid)
self.view.addItem(ygrid)
self.view.addItem(zgrid)
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| xgrid.scale(12.8, 12.8, 12.8)
ygrid.scale(12.8, 12.8, 12.8)
zgrid.scale(12.8, 12.8, 12.8) | [
"xgrid",
".",
"scale",
"(",
"12",
".",
"8",
"12",
".",
"8",
"12",
".",
"8",
")",
"ygrid",
".",
"scale",
"(",
"12",
".",
"8",
"12",
".",
"8",
"12",
".",
"8",
")",
"zgrid",
".",
"scale",
"(",
"12",
".",
"8",
"12",
".",
"8",
"12",
".",
"8",
")"
]
| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/GLMultiDimension.py#L507-L727 |
GeoPyTool/GeoPyTool | geopytool/XY.py | XY.create_main_frame | def create_main_frame(self):
self.resize(800, 800)
self.main_frame = QWidget()
self.dpi = 128
self.fig = Figure((8.0, 8.0), dpi=self.dpi)
self.fig.subplots_adjust(hspace=0.5, wspace=0.5, left=0.13, bottom=0.2, right=0.7, top=0.9)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
self.axes = self.fig.add_subplot(111)
# self.axes.hold(False)
# Create the navigation toolbar, tied to the canvas
self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
# Other GUI controls
self.load_data_button = QPushButton('&Add Data to Compare')
self.load_data_button.clicked.connect(self.loadDataToTest)
self.save_plot_button = QPushButton('&Save IMG')
self.save_plot_button .clicked.connect(self.saveImgFile)
self.stat_button = QPushButton('&Show Stat')
self.stat_button.clicked.connect(self.Stat)
self.load_img_button = QPushButton('&Load Basemap')
self.load_img_button.clicked.connect(self.Load)
self.unload_img_button = QPushButton('&Unload Basemap')
self.unload_img_button.clicked.connect(self.Unload)
self.legend_cb = QCheckBox('&Legend')
self.legend_cb.setChecked(True)
self.legend_cb.stateChanged.connect(self.Magic) # int
self.show_load_data_cb = QCheckBox('&Show Loaded Data')
self.show_load_data_cb.setChecked(True)
self.show_load_data_cb.stateChanged.connect(self.Magic) # int
self.show_data_index_cb = QCheckBox('&Show Data Index')
self.show_data_index_cb.setChecked(False)
self.show_data_index_cb.stateChanged.connect(self.Magic) # int
self.hyperplane_cb= QCheckBox('&Hyperplane')
self.hyperplane_cb.setChecked(False)
self.hyperplane_cb.stateChanged.connect(self.Magic) # int
self.fit_cb= QCheckBox('&PolyFit')
self.fit_cb.setChecked(False)
self.fit_cb.stateChanged.connect(self.Magic) # int
self.fit_seter = QLineEdit(self)
self.fit_seter.textChanged[str].connect(self.FitChanged)
self.fit_slider_label = QLabel('y= f(x) EXP')
self.fit_slider = QSlider(Qt.Vertical)
self.fit_slider.setRange(0, 1)
self.fit_slider.setValue(0)
self.fit_slider.setTracking(True)
self.fit_slider.setTickPosition(QSlider.TicksBothSides)
self.fit_slider.valueChanged.connect(self.Magic) # int
self.shape_cb= QCheckBox('&Shape')
self.shape_cb.setChecked(False)
self.shape_cb.stateChanged.connect(self.Magic) # int
#self.shape_label = QLabel('Step')
#self.shape_seter = QLineEdit(self)
#self.shape_seter.textChanged[str].connect(self.ShapeChanged)
self.norm_cb = QCheckBox('&Norm')
self.norm_cb.setChecked(False)
self.norm_cb.stateChanged.connect(self.Magic) # int
self.standard_slider = QSlider(Qt.Horizontal)
self.standard_slider.setRange(0, len(self.StandardsName))
if len(self._given_Standard) > 0:
self.standard_slider.setValue(len(self.StandardsName))
self.right_label = QLabel("Self Defined Standard")
else:
self.standard_slider.setValue(0)
self.right_label = QLabel(self.StandardsName[int(self.standard_slider.value())])
self.standard_slider.setTracking(True)
self.standard_slider.setTickPosition(QSlider.TicksBothSides)
self.standard_slider.valueChanged.connect(self.Magic) # int
self.left_label= QLabel('Standard' )
self.x_element = QSlider(Qt.Horizontal)
self.x_element.setRange(0, len(self.items) - 1)
self.x_element.setValue(0)
self.x_element.setTracking(True)
self.x_element.setTickPosition(QSlider.TicksBothSides)
self.x_element.valueChanged.connect(self.ValueChooser) # int
self.x_seter = QLineEdit(self)
self.x_seter.textChanged[str].connect(self.LabelSeter)
#self.x_calculator = QLineEdit(self)
self.logx_cb = QCheckBox('&Log')
self.logx_cb.setChecked(False)
self.logx_cb.stateChanged.connect(self.Magic) # int
self.y_element = QSlider(Qt.Horizontal)
self.y_element.setRange(0, len(self.items) - 1)
self.y_element.setValue(1)
self.y_element.setTracking(True)
self.y_element.setTickPosition(QSlider.TicksBothSides)
self.y_element.valueChanged.connect(self.ValueChooser) # int
self.y_seter = QLineEdit(self)
self.y_seter.textChanged[str].connect(self.LabelSeter)
#self.y_calculator = QLineEdit(self)
self.logy_cb = QCheckBox('&Log')
self.logy_cb.setChecked(False)
self.logy_cb.stateChanged.connect(self.Magic) # int
self.hyperplane_cb= QCheckBox('&Hyperplane')
self.hyperplane_cb.setChecked(False)
self.hyperplane_cb.stateChanged.connect(self.Magic) # int
self.save_predict_button_selected = QPushButton('&Predict Selected')
self.save_predict_button_selected.clicked.connect(self.showPredictResultSelected)
self.save_predict_button = QPushButton('&Predict All')
self.save_predict_button.clicked.connect(self.showPredictResult)
self.load_data_button = QPushButton('&Add Data to Compare')
self.load_data_button.clicked.connect(self.loadDataToTest)
self.width_size_seter_label = QLabel('SVG Width')
self.width_size_seter = QLineEdit(self)
self.width_size_seter.textChanged[str].connect(self.WChanged)
self.height_size_seter_label = QLabel('SVG Height')
self.height_size_seter = QLineEdit(self)
self.height_size_seter.textChanged[str].connect(self.HChanged)
self.Left_size_seter_label = QLabel('PNG Left')
self.Left_size_seter = QLineEdit(self)
self.Left_size_seter.textChanged[str].connect(self.LeftChanged)
self.Right_size_seter_label = QLabel('PNG Right')
self.Right_size_seter = QLineEdit(self)
self.Right_size_seter.textChanged[str].connect(self.RightChanged)
self.Up_size_seter_label = QLabel('PNG Top')
self.Up_size_seter = QLineEdit(self)
self.Up_size_seter.textChanged[str].connect(self.UpChanged)
self.Down_size_seter_label = QLabel('PNG Bottom')
self.Down_size_seter = QLineEdit(self)
self.Down_size_seter.textChanged[str].connect(self.DownChanged)
#
# Layout with box sizers
#
self.hbox = QHBoxLayout()
self.hbox0 = QHBoxLayout()
self.hbox1 = QHBoxLayout()
self.hbox2 = QHBoxLayout()
self.hbox3 = QHBoxLayout()
self.hbox4 = QHBoxLayout()
self.hbox5 = QHBoxLayout()
w=self.width()
h=self.height()
#self.load_data_button.setFixedWidth(w/4)
self.kernel_select = QSlider(Qt.Horizontal)
self.kernel_select.setRange(0, len(self.kernel_list)-1)
self.kernel_select.setValue(0)
self.kernel_select.setTracking(True)
self.kernel_select.setTickPosition(QSlider.TicksBothSides)
self.kernel_select.valueChanged.connect(self.Magic) # int
self.kernel_select_label = QLabel('Kernel')
for w in [self.save_plot_button ,self.stat_button,self.load_data_button,self.save_predict_button,self.save_predict_button_selected]:
self.hbox.addWidget(w)
self.hbox.setAlignment(w, Qt.AlignVCenter)
for w in [self.legend_cb,self.show_load_data_cb,self.show_data_index_cb, self.norm_cb,self.shape_cb,self.hyperplane_cb,self.kernel_select_label,self.kernel_select]:
self.hbox0.addWidget(w)
self.hbox0.setAlignment(w, Qt.AlignVCenter)
for w in [self.left_label, self.standard_slider,self.right_label,self.fit_cb,self.fit_slider,self.fit_slider_label ,self.fit_seter]:
self.hbox1.addWidget(w)
self.hbox1.setAlignment(w, Qt.AlignVCenter)
for w in [self.logx_cb,self.x_seter, self.x_element]:
self.hbox2.addWidget(w)
self.hbox2.setAlignment(w, Qt.AlignVCenter)
for w in [self.logy_cb,self.y_seter, self.y_element]:
self.hbox3.addWidget(w)
self.hbox3.setAlignment(w, Qt.AlignVCenter)
for w in [self.load_img_button, self.width_size_seter_label, self.width_size_seter, self.height_size_seter_label,
self.height_size_seter]:
self.hbox4.addWidget(w)
self.hbox4.setAlignment(w, Qt.AlignLeft)
for w in [self.unload_img_button,self.Left_size_seter_label, self.Left_size_seter,
self.Right_size_seter_label, self.Right_size_seter,self.Down_size_seter_label, self.Down_size_seter,
self.Up_size_seter_label ,self.Up_size_seter]:
self.hbox5.addWidget(w)
self.hbox5.setAlignment(w, Qt.AlignLeft)
self.vbox = QVBoxLayout()
self.vbox.addWidget(self.mpl_toolbar)
self.vbox.addWidget(self.canvas)
self.vbox.addLayout(self.hbox)
self.vbox.addLayout(self.hbox0)
self.vbox.addLayout(self.hbox1)
self.vbox.addLayout(self.hbox2)
self.vbox.addLayout(self.hbox3)
self.vbox.addLayout(self.hbox4)
self.vbox.addLayout(self.hbox5)
self.textbox = GrowingTextEdit(self)
self.vbox.addWidget(self.textbox)
self.main_frame.setLayout(self.vbox)
self.setCentralWidget(self.main_frame)
w=self.width()
h=self.height()
self.x_seter.setFixedWidth(w/10)
self.y_seter.setFixedWidth(w/10)
'''
self.save_plot_button.setFixedWidth(w/10)
self.stat_button.setFixedWidth(w/10)
self.load_data_button.setFixedWidth(w/4)
self.save_predict_button_selected.setFixedWidth(w/4)
self.save_predict_button.setFixedWidth(w/4)
'''
self.standard_slider.setFixedWidth(w/5)
self.right_label.setFixedWidth(w/5)
self.fit_seter.setFixedWidth(w/20)
self.load_img_button.setFixedWidth(w/5)
self.unload_img_button.setFixedWidth(w/5)
self.width_size_seter_label.setFixedWidth(w/10)
self.height_size_seter_label.setFixedWidth(w/10)
self.width_size_seter.setMinimumWidth(w/20)
self.height_size_seter.setMinimumWidth(w/20)
self.Right_size_seter_label.setFixedWidth(w/10)
self.Left_size_seter_label.setFixedWidth(w/10)
self.Up_size_seter_label.setFixedWidth(w/10)
self.Down_size_seter_label.setFixedWidth(w/10)
self.Right_size_seter.setFixedWidth(w/20)
self.Left_size_seter.setFixedWidth(w/20)
self.Up_size_seter.setFixedWidth(w/20)
self.Down_size_seter.setFixedWidth(w/20) | python | def create_main_frame(self):
self.resize(800, 800)
self.main_frame = QWidget()
self.dpi = 128
self.fig = Figure((8.0, 8.0), dpi=self.dpi)
self.fig.subplots_adjust(hspace=0.5, wspace=0.5, left=0.13, bottom=0.2, right=0.7, top=0.9)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
self.axes = self.fig.add_subplot(111)
self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
self.load_data_button = QPushButton('&Add Data to Compare')
self.load_data_button.clicked.connect(self.loadDataToTest)
self.save_plot_button = QPushButton('&Save IMG')
self.save_plot_button .clicked.connect(self.saveImgFile)
self.stat_button = QPushButton('&Show Stat')
self.stat_button.clicked.connect(self.Stat)
self.load_img_button = QPushButton('&Load Basemap')
self.load_img_button.clicked.connect(self.Load)
self.unload_img_button = QPushButton('&Unload Basemap')
self.unload_img_button.clicked.connect(self.Unload)
self.legend_cb = QCheckBox('&Legend')
self.legend_cb.setChecked(True)
self.legend_cb.stateChanged.connect(self.Magic)
self.show_load_data_cb = QCheckBox('&Show Loaded Data')
self.show_load_data_cb.setChecked(True)
self.show_load_data_cb.stateChanged.connect(self.Magic)
self.show_data_index_cb = QCheckBox('&Show Data Index')
self.show_data_index_cb.setChecked(False)
self.show_data_index_cb.stateChanged.connect(self.Magic)
self.hyperplane_cb= QCheckBox('&Hyperplane')
self.hyperplane_cb.setChecked(False)
self.hyperplane_cb.stateChanged.connect(self.Magic)
self.fit_cb= QCheckBox('&PolyFit')
self.fit_cb.setChecked(False)
self.fit_cb.stateChanged.connect(self.Magic)
self.fit_seter = QLineEdit(self)
self.fit_seter.textChanged[str].connect(self.FitChanged)
self.fit_slider_label = QLabel('y= f(x) EXP')
self.fit_slider = QSlider(Qt.Vertical)
self.fit_slider.setRange(0, 1)
self.fit_slider.setValue(0)
self.fit_slider.setTracking(True)
self.fit_slider.setTickPosition(QSlider.TicksBothSides)
self.fit_slider.valueChanged.connect(self.Magic)
self.shape_cb= QCheckBox('&Shape')
self.shape_cb.setChecked(False)
self.shape_cb.stateChanged.connect(self.Magic)
self.norm_cb = QCheckBox('&Norm')
self.norm_cb.setChecked(False)
self.norm_cb.stateChanged.connect(self.Magic)
self.standard_slider = QSlider(Qt.Horizontal)
self.standard_slider.setRange(0, len(self.StandardsName))
if len(self._given_Standard) > 0:
self.standard_slider.setValue(len(self.StandardsName))
self.right_label = QLabel("Self Defined Standard")
else:
self.standard_slider.setValue(0)
self.right_label = QLabel(self.StandardsName[int(self.standard_slider.value())])
self.standard_slider.setTracking(True)
self.standard_slider.setTickPosition(QSlider.TicksBothSides)
self.standard_slider.valueChanged.connect(self.Magic)
self.left_label= QLabel('Standard' )
self.x_element = QSlider(Qt.Horizontal)
self.x_element.setRange(0, len(self.items) - 1)
self.x_element.setValue(0)
self.x_element.setTracking(True)
self.x_element.setTickPosition(QSlider.TicksBothSides)
self.x_element.valueChanged.connect(self.ValueChooser)
self.x_seter = QLineEdit(self)
self.x_seter.textChanged[str].connect(self.LabelSeter)
self.logx_cb = QCheckBox('&Log')
self.logx_cb.setChecked(False)
self.logx_cb.stateChanged.connect(self.Magic)
self.y_element = QSlider(Qt.Horizontal)
self.y_element.setRange(0, len(self.items) - 1)
self.y_element.setValue(1)
self.y_element.setTracking(True)
self.y_element.setTickPosition(QSlider.TicksBothSides)
self.y_element.valueChanged.connect(self.ValueChooser)
self.y_seter = QLineEdit(self)
self.y_seter.textChanged[str].connect(self.LabelSeter)
self.logy_cb = QCheckBox('&Log')
self.logy_cb.setChecked(False)
self.logy_cb.stateChanged.connect(self.Magic)
self.hyperplane_cb= QCheckBox('&Hyperplane')
self.hyperplane_cb.setChecked(False)
self.hyperplane_cb.stateChanged.connect(self.Magic)
self.save_predict_button_selected = QPushButton('&Predict Selected')
self.save_predict_button_selected.clicked.connect(self.showPredictResultSelected)
self.save_predict_button = QPushButton('&Predict All')
self.save_predict_button.clicked.connect(self.showPredictResult)
self.load_data_button = QPushButton('&Add Data to Compare')
self.load_data_button.clicked.connect(self.loadDataToTest)
self.width_size_seter_label = QLabel('SVG Width')
self.width_size_seter = QLineEdit(self)
self.width_size_seter.textChanged[str].connect(self.WChanged)
self.height_size_seter_label = QLabel('SVG Height')
self.height_size_seter = QLineEdit(self)
self.height_size_seter.textChanged[str].connect(self.HChanged)
self.Left_size_seter_label = QLabel('PNG Left')
self.Left_size_seter = QLineEdit(self)
self.Left_size_seter.textChanged[str].connect(self.LeftChanged)
self.Right_size_seter_label = QLabel('PNG Right')
self.Right_size_seter = QLineEdit(self)
self.Right_size_seter.textChanged[str].connect(self.RightChanged)
self.Up_size_seter_label = QLabel('PNG Top')
self.Up_size_seter = QLineEdit(self)
self.Up_size_seter.textChanged[str].connect(self.UpChanged)
self.Down_size_seter_label = QLabel('PNG Bottom')
self.Down_size_seter = QLineEdit(self)
self.Down_size_seter.textChanged[str].connect(self.DownChanged)
self.hbox = QHBoxLayout()
self.hbox0 = QHBoxLayout()
self.hbox1 = QHBoxLayout()
self.hbox2 = QHBoxLayout()
self.hbox3 = QHBoxLayout()
self.hbox4 = QHBoxLayout()
self.hbox5 = QHBoxLayout()
w=self.width()
h=self.height()
self.kernel_select = QSlider(Qt.Horizontal)
self.kernel_select.setRange(0, len(self.kernel_list)-1)
self.kernel_select.setValue(0)
self.kernel_select.setTracking(True)
self.kernel_select.setTickPosition(QSlider.TicksBothSides)
self.kernel_select.valueChanged.connect(self.Magic)
self.kernel_select_label = QLabel('Kernel')
for w in [self.save_plot_button ,self.stat_button,self.load_data_button,self.save_predict_button,self.save_predict_button_selected]:
self.hbox.addWidget(w)
self.hbox.setAlignment(w, Qt.AlignVCenter)
for w in [self.legend_cb,self.show_load_data_cb,self.show_data_index_cb, self.norm_cb,self.shape_cb,self.hyperplane_cb,self.kernel_select_label,self.kernel_select]:
self.hbox0.addWidget(w)
self.hbox0.setAlignment(w, Qt.AlignVCenter)
for w in [self.left_label, self.standard_slider,self.right_label,self.fit_cb,self.fit_slider,self.fit_slider_label ,self.fit_seter]:
self.hbox1.addWidget(w)
self.hbox1.setAlignment(w, Qt.AlignVCenter)
for w in [self.logx_cb,self.x_seter, self.x_element]:
self.hbox2.addWidget(w)
self.hbox2.setAlignment(w, Qt.AlignVCenter)
for w in [self.logy_cb,self.y_seter, self.y_element]:
self.hbox3.addWidget(w)
self.hbox3.setAlignment(w, Qt.AlignVCenter)
for w in [self.load_img_button, self.width_size_seter_label, self.width_size_seter, self.height_size_seter_label,
self.height_size_seter]:
self.hbox4.addWidget(w)
self.hbox4.setAlignment(w, Qt.AlignLeft)
for w in [self.unload_img_button,self.Left_size_seter_label, self.Left_size_seter,
self.Right_size_seter_label, self.Right_size_seter,self.Down_size_seter_label, self.Down_size_seter,
self.Up_size_seter_label ,self.Up_size_seter]:
self.hbox5.addWidget(w)
self.hbox5.setAlignment(w, Qt.AlignLeft)
self.vbox = QVBoxLayout()
self.vbox.addWidget(self.mpl_toolbar)
self.vbox.addWidget(self.canvas)
self.vbox.addLayout(self.hbox)
self.vbox.addLayout(self.hbox0)
self.vbox.addLayout(self.hbox1)
self.vbox.addLayout(self.hbox2)
self.vbox.addLayout(self.hbox3)
self.vbox.addLayout(self.hbox4)
self.vbox.addLayout(self.hbox5)
self.textbox = GrowingTextEdit(self)
self.vbox.addWidget(self.textbox)
self.main_frame.setLayout(self.vbox)
self.setCentralWidget(self.main_frame)
w=self.width()
h=self.height()
self.x_seter.setFixedWidth(w/10)
self.y_seter.setFixedWidth(w/10)
self.standard_slider.setFixedWidth(w/5)
self.right_label.setFixedWidth(w/5)
self.fit_seter.setFixedWidth(w/20)
self.load_img_button.setFixedWidth(w/5)
self.unload_img_button.setFixedWidth(w/5)
self.width_size_seter_label.setFixedWidth(w/10)
self.height_size_seter_label.setFixedWidth(w/10)
self.width_size_seter.setMinimumWidth(w/20)
self.height_size_seter.setMinimumWidth(w/20)
self.Right_size_seter_label.setFixedWidth(w/10)
self.Left_size_seter_label.setFixedWidth(w/10)
self.Up_size_seter_label.setFixedWidth(w/10)
self.Down_size_seter_label.setFixedWidth(w/10)
self.Right_size_seter.setFixedWidth(w/20)
self.Left_size_seter.setFixedWidth(w/20)
self.Up_size_seter.setFixedWidth(w/20)
self.Down_size_seter.setFixedWidth(w/20) | [
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| self.save_plot_button.setFixedWidth(w/10)
self.stat_button.setFixedWidth(w/10)
self.load_data_button.setFixedWidth(w/4)
self.save_predict_button_selected.setFixedWidth(w/4)
self.save_predict_button.setFixedWidth(w/4) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/XY.py#L176-L498 |
GeoPyTool/GeoPyTool | geopytool/QAPF.py | QAPF.create_main_frame | def create_main_frame(self):
self.resize(800, 1000)
self.main_frame = QWidget()
self.dpi = 128
self.fig = Figure((12, 11), dpi=self.dpi)
self.fig.subplots_adjust(hspace=0.5, wspace=0.5, left=0.1, bottom=0.2, right=0.7, top=0.9)
# 8 * np.sqrt(3)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
self.axes = self.fig.add_subplot(111)
self.axes.axis('off')
self.axes.set_xlim(-10, 110)
self.axes.set_ylim(-105 * np.sqrt(3) / 2, 105 * np.sqrt(3) / 2)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
self.axes = self.fig.add_subplot(111)
# Create the navigation toolbar, tied to the canvas
self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
# Other GUI controls
self.save_button = QPushButton('&Save')
self.save_button.clicked.connect(self.saveImgFile)
#self.result_button = QPushButton('&Result')
#self.result_button.clicked.connect(self.Explain)
self.legend_cb = QCheckBox('&Legend')
self.legend_cb.setChecked(True)
self.legend_cb.stateChanged.connect(self.QAPF) # int
self.slider_left_label = QLabel('Plutonic')
self.slider_right_label = QLabel('Volcanic')
self.slider = QSlider(Qt.Horizontal)
self.slider.setRange(0, 1)
self.slider.setValue(0)
self.slider.setTracking(True)
self.slider.setTickPosition(QSlider.TicksBothSides)
self.slider.valueChanged.connect(self.QAPF) # int
'''
self.Tag_cb = QCheckBox('&Plutonic')
self.Tag_cb.setChecked(True)
self.Tag_cb.stateChanged.connect(self.QAPF) # int
if (self.Tag_cb.isChecked()):
self.Tag_cb.setText('&Plutonic')
else:
self.Tag_cb.setText('&Volcanic')
'''
self.detail_cb = QCheckBox('&Detail')
self.detail_cb.setChecked(True)
self.detail_cb.stateChanged.connect(self.QAPF) # int
#
# Layout with box sizers
#
self.hbox = QHBoxLayout()
for w in [self.save_button, self.detail_cb, self.legend_cb,self.slider_left_label,self.slider,self.slider_right_label]:
self.hbox.addWidget(w)
self.hbox.setAlignment(w, Qt.AlignVCenter)
self.vbox = QVBoxLayout()
self.vbox.addWidget(self.mpl_toolbar)
self.vbox.addWidget(self.canvas)
self.vbox.addLayout(self.hbox)
self.textbox = GrowingTextEdit(self)
self.vbox.addWidget(self.textbox)
self.main_frame.setLayout(self.vbox)
self.setCentralWidget(self.main_frame)
w=self.width()
h=self.height()
#setFixedWidth(w/10)
self.slider.setMinimumWidth(w/10)
self.slider_left_label.setMinimumWidth(w/10)
self.slider_right_label.setMinimumWidth(w/10) | python | def create_main_frame(self):
self.resize(800, 1000)
self.main_frame = QWidget()
self.dpi = 128
self.fig = Figure((12, 11), dpi=self.dpi)
self.fig.subplots_adjust(hspace=0.5, wspace=0.5, left=0.1, bottom=0.2, right=0.7, top=0.9)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
self.axes = self.fig.add_subplot(111)
self.axes.axis('off')
self.axes.set_xlim(-10, 110)
self.axes.set_ylim(-105 * np.sqrt(3) / 2, 105 * np.sqrt(3) / 2)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
self.axes = self.fig.add_subplot(111)
self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
self.save_button = QPushButton('&Save')
self.save_button.clicked.connect(self.saveImgFile)
self.legend_cb = QCheckBox('&Legend')
self.legend_cb.setChecked(True)
self.legend_cb.stateChanged.connect(self.QAPF)
self.slider_left_label = QLabel('Plutonic')
self.slider_right_label = QLabel('Volcanic')
self.slider = QSlider(Qt.Horizontal)
self.slider.setRange(0, 1)
self.slider.setValue(0)
self.slider.setTracking(True)
self.slider.setTickPosition(QSlider.TicksBothSides)
self.slider.valueChanged.connect(self.QAPF)
self.detail_cb = QCheckBox('&Detail')
self.detail_cb.setChecked(True)
self.detail_cb.stateChanged.connect(self.QAPF)
self.hbox = QHBoxLayout()
for w in [self.save_button, self.detail_cb, self.legend_cb,self.slider_left_label,self.slider,self.slider_right_label]:
self.hbox.addWidget(w)
self.hbox.setAlignment(w, Qt.AlignVCenter)
self.vbox = QVBoxLayout()
self.vbox.addWidget(self.mpl_toolbar)
self.vbox.addWidget(self.canvas)
self.vbox.addLayout(self.hbox)
self.textbox = GrowingTextEdit(self)
self.vbox.addWidget(self.textbox)
self.main_frame.setLayout(self.vbox)
self.setCentralWidget(self.main_frame)
w=self.width()
h=self.height()
self.slider.setMinimumWidth(w/10)
self.slider_left_label.setMinimumWidth(w/10)
self.slider_right_label.setMinimumWidth(w/10) | [
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| self.Tag_cb = QCheckBox('&Plutonic')
self.Tag_cb.setChecked(True)
self.Tag_cb.stateChanged.connect(self.QAPF) # int
if (self.Tag_cb.isChecked()):
self.Tag_cb.setText('&Plutonic')
else:
self.Tag_cb.setText('&Volcanic') | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/QAPF.py#L211-L303 |
GeoPyTool/GeoPyTool | geopytool/CustomClass.py | Tool.TriToBin | def TriToBin(self, x, y, z):
'''
Turn an x-y-z triangular coord to an a-b coord.
if z is negative, calc with its abs then return (a, -b).
:param x,y,z: the three numbers of the triangular coord
:type x,y,z: float or double are both OK, just numbers
:return: the corresponding a-b coord
:rtype: a tuple consist of a and b
'''
if (z >= 0):
if (x + y + z == 0):
return (0, 0)
else:
Sum = x + y + z
X = 100.0 * x / Sum
Y = 100.0 * y / Sum
Z = 100.0 * z / Sum
if (X + Y != 0):
a = Z / 2.0 + (100.0 - Z) * Y / (Y + X)
else:
a = Z / 2.0
b = Z / 2.0 * (np.sqrt(3))
return (a, b)
else:
z = abs(z)
if (x + y + z == 0):
return (0, 0)
else:
Sum = x + y + z
X = 100.0 * x / Sum
Y = 100.0 * y / Sum
Z = 100.0 * z / Sum
if (X + Y != 0):
a = Z / 2.0 + (100.0 - Z) * Y / (Y + X)
else:
a = Z / 2.0
b = Z / 2.0 * (np.sqrt(3))
return (a, -b) | python | def TriToBin(self, x, y, z):
if (z >= 0):
if (x + y + z == 0):
return (0, 0)
else:
Sum = x + y + z
X = 100.0 * x / Sum
Y = 100.0 * y / Sum
Z = 100.0 * z / Sum
if (X + Y != 0):
a = Z / 2.0 + (100.0 - Z) * Y / (Y + X)
else:
a = Z / 2.0
b = Z / 2.0 * (np.sqrt(3))
return (a, b)
else:
z = abs(z)
if (x + y + z == 0):
return (0, 0)
else:
Sum = x + y + z
X = 100.0 * x / Sum
Y = 100.0 * y / Sum
Z = 100.0 * z / Sum
if (X + Y != 0):
a = Z / 2.0 + (100.0 - Z) * Y / (Y + X)
else:
a = Z / 2.0
b = Z / 2.0 * (np.sqrt(3))
return (a, -b) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/CustomClass.py#L28-L67 |
GeoPyTool/GeoPyTool | geopytool/CustomClass.py | Tool.BinToTri | def BinToTri(self, a, b):
'''
Turn an a-b coord to an x-y-z triangular coord .
if z is negative, calc with its abs then return (a, -b).
:param a,b: the numbers of the a-b coord
:type a,b: float or double are both OK, just numbers
:return: the corresponding x-y-z triangular coord
:rtype: a tuple consist of x,y,z
'''
if (b >= 0):
y = a - b / np.sqrt(3)
z = b * 2 / np.sqrt(3)
x = 100 - (a + b / np.sqrt(3))
return (x, y, z)
else:
y = a + b / np.sqrt(3)
z = b * 2 / np.sqrt(3)
x = 100 - (a - b / np.sqrt(3))
return (x, y, z) | python | def BinToTri(self, a, b):
if (b >= 0):
y = a - b / np.sqrt(3)
z = b * 2 / np.sqrt(3)
x = 100 - (a + b / np.sqrt(3))
return (x, y, z)
else:
y = a + b / np.sqrt(3)
z = b * 2 / np.sqrt(3)
x = 100 - (a - b / np.sqrt(3))
return (x, y, z) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/CustomClass.py#L69-L89 |
GeoPyTool/GeoPyTool | geopytool/CustomClass.py | Tool.Cross | def Cross(self, A=[(0, 0), (10, 10)], B=[(0, 10), (100, 0)]):
'''
Return the crosspoint of two line A and B.
:param A: first line
:type A: a list consist of two tuples, beginning and end point of the line
:param B: second line
:type B: a list consist of two tuples, beginning and end point of the line
:return: the crosspoint of A and B
:rtype: a list consist of two numbers, the x-y of the crosspoint
'''
x0, y0 = A[0]
x1, y1 = A[1]
x2, y2 = B[0]
x3, y3 = B[1]
b1 = (y1 - y0) / (x1 - x0)
b2 = (y3 - y2) / (x3 - x2)
c1 = y0 - b1 * x0
c2 = y2 - b2 * x2
x = (c2 - c1) / (b1 - b2)
y = b1 * x + c1
return ([x, y]) | python | def Cross(self, A=[(0, 0), (10, 10)], B=[(0, 10), (100, 0)]):
x0, y0 = A[0]
x1, y1 = A[1]
x2, y2 = B[0]
x3, y3 = B[1]
b1 = (y1 - y0) / (x1 - x0)
b2 = (y3 - y2) / (x3 - x2)
c1 = y0 - b1 * x0
c2 = y2 - b2 * x2
x = (c2 - c1) / (b1 - b2)
y = b1 * x + c1
return ([x, y]) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/CustomClass.py#L91-L116 |
GeoPyTool/GeoPyTool | geopytool/CustomClass.py | Tool.TriCross | def TriCross(self, A=[(100, 0, 0), (0, 50, 60)], B=[(50, 50, 0), (0, 0, 100)]):
'''
Return the crosspoint of two line A and B in triangular coord.
:param A: first line
:type A: a list consist of two tuples, beginning and end point of the line
:param B: second line
:type B: a list consist of two tuples, beginning and end point of the line
:return: the crosspoint of A and B
:rtype: a list consist of three numbers, the x-y-z of the triangular coord
'''
x0, y0 = self.TriToBin(A[0][0], A[0][1], A[0][2])
x1, y1 = self.TriToBin(A[1][0], A[1][1], A[1][2])
x2, y2 = self.TriToBin(B[0][0], B[0][1], B[0][2])
x3, y3 = self.TriToBin(B[1][0], B[1][1], B[1][2])
b1 = (y1 - y0) / (x1 - x0)
b2 = (y3 - y2) / (x3 - x2)
c1 = y0 - b1 * x0
c2 = y2 - b2 * x2
x = (c2 - c1) / (b1 - b2)
y = b1 * x + c1
result = self.BinToTri(x, y)
return (result) | python | def TriCross(self, A=[(100, 0, 0), (0, 50, 60)], B=[(50, 50, 0), (0, 0, 100)]):
x0, y0 = self.TriToBin(A[0][0], A[0][1], A[0][2])
x1, y1 = self.TriToBin(A[1][0], A[1][1], A[1][2])
x2, y2 = self.TriToBin(B[0][0], B[0][1], B[0][2])
x3, y3 = self.TriToBin(B[1][0], B[1][1], B[1][2])
b1 = (y1 - y0) / (x1 - x0)
b2 = (y3 - y2) / (x3 - x2)
c1 = y0 - b1 * x0
c2 = y2 - b2 * x2
x = (c2 - c1) / (b1 - b2)
y = b1 * x + c1
result = self.BinToTri(x, y)
return (result) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/CustomClass.py#L118-L144 |
GeoPyTool/GeoPyTool | geopytool/CustomClass.py | Tool.Fill | def Fill(self, P=[(100, 0), (85, 15), (0, 3)], Color='blue', Alpha=0.3):
'''
Fill a region in planimetric rectangular coord.
:param P: the peak points of the region in planimetric rectangular coord
:type P: a list consist of at least three tuples, which are the points in planimetric rectangular coord
:param Color: the color used to fill the region
:type Color: a string; b: blue, g: green, r: red, c: cyan, m: magenta, y: yellow, k: black, w: white
:param Alpha: the transparency used to fill the region
:type Alpha: a float number from 0 to 1, higher darker, lower more transparent
'''
a = []
b = []
for i in P:
a.append(i[0])
b.append(i[1])
return (a, b) | python | def Fill(self, P=[(100, 0), (85, 15), (0, 3)], Color='blue', Alpha=0.3):
a = []
b = []
for i in P:
a.append(i[0])
b.append(i[1])
return (a, b) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/CustomClass.py#L146-L164 |
GeoPyTool/GeoPyTool | geopytool/CustomClass.py | Tool.TriFill | def TriFill(self, P=[(100, 0, 0), (85, 15, 0), (0, 3, 97)], Color='blue', Alpha=0.3):
'''
Fill a region in triangular coord.
:param P: the peak points of the region in triangular coord
:type P: a list consist of at least three tuples, which are the points in triangular coord
:param Color: the color used to fill the region
:type Color: a string; b: blue, g: green, r: red, c: cyan, m: magenta, y: yellow, k: black, w: white
:param Alpha: the transparency used to fill the region
:type Alpha: a float number from 0 to 1, higher darker, lower more transparent
'''
a = []
b = []
for i in P:
a.append(self.TriToBin(i[0], i[1], i[2])[0])
b.append(self.TriToBin(i[0], i[1], i[2])[1])
return (a, b) | python | def TriFill(self, P=[(100, 0, 0), (85, 15, 0), (0, 3, 97)], Color='blue', Alpha=0.3):
a = []
b = []
for i in P:
a.append(self.TriToBin(i[0], i[1], i[2])[0])
b.append(self.TriToBin(i[0], i[1], i[2])[1])
return (a, b) | [
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| Fill a region in triangular coord.
:param P: the peak points of the region in triangular coord
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:param Color: the color used to fill the region
:type Color: a string; b: blue, g: green, r: red, c: cyan, m: magenta, y: yellow, k: black, w: white
:param Alpha: the transparency used to fill the region
:type Alpha: a float number from 0 to 1, higher darker, lower more transparent | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/CustomClass.py#L166-L185 |
GeoPyTool/GeoPyTool | geopytool/CustomClass.py | Line.sequence | def sequence(self):
'''
sort the points in the line with given option
'''
if (len(self.Points[0]) == 2):
if (self.Sort == 'X' or self.Sort == 'x'):
self.Points.sort(key=lambda x: x[0])
self.order(self.Points)
elif (self.Sort == 'Y' or self.Sort == 'y'):
self.Points.sort(key=lambda x: x[1])
self.order(self.Points)
else:
self.order(self.Points)
if (len(self.Points[0]) == 3):
if (self.Sort == 'X' or self.Sort == 'x'):
self.Points.sort(key=lambda x: x[0])
self.order(self.Points)
elif (self.Sort == 'Y' or self.Sort == 'y'):
self.Points.sort(key=lambda x: x[1])
self.order(self.Points)
elif (self.Sort == 'Z' or self.Sort == 'Z'):
self.Points.sort(key=lambda x: x[2])
self.order(self.Points)
else:
self.order(self.Points) | python | def sequence(self):
if (len(self.Points[0]) == 2):
if (self.Sort == 'X' or self.Sort == 'x'):
self.Points.sort(key=lambda x: x[0])
self.order(self.Points)
elif (self.Sort == 'Y' or self.Sort == 'y'):
self.Points.sort(key=lambda x: x[1])
self.order(self.Points)
else:
self.order(self.Points)
if (len(self.Points[0]) == 3):
if (self.Sort == 'X' or self.Sort == 'x'):
self.Points.sort(key=lambda x: x[0])
self.order(self.Points)
elif (self.Sort == 'Y' or self.Sort == 'y'):
self.Points.sort(key=lambda x: x[1])
self.order(self.Points)
elif (self.Sort == 'Z' or self.Sort == 'Z'):
self.Points.sort(key=lambda x: x[2])
self.order(self.Points)
else:
self.order(self.Points) | [
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| sort the points in the line with given option | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/CustomClass.py#L367-L394 |
GeoPyTool/GeoPyTool | geopytool/CustomClass.py | TableViewer.resizeEvent | def resizeEvent(self, evt=None):
w = self.width()
h = self.height()
'''
if h<=360:
h=360
self.resize(w,h)
if w<=640:
w = 640
self.resize(w, h)
'''
step = (w * 94 / 100) / 5
foot = h * 3 / 48 | python | def resizeEvent(self, evt=None):
w = self.width()
h = self.height()
step = (w * 94 / 100) / 5
foot = h * 3 / 48 | [
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self.resize(w, h) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/CustomClass.py#L813-L827 |
GeoPyTool/GeoPyTool | geopytool/K2OSiO2.py | K2OSiO2.K2OSiO2 | def K2OSiO2(self, Left=35, Right=79, X0=30, X1=90, X_Gap=7, Base=0,
Top=19, Y0=1, Y1=19, Y_Gap=19, FontSize=12, xlabel=r'$SiO_2 wt\%$', ylabel=r'$K_2O wt\%$', width=12,
height=12, dpi=300):
self.setWindowTitle('K2OSiO2 diagram ')
self.axes.clear()
#self.axes.axis('off')
self.axes.set_xlabel(self.xlabel)
self.axes.set_ylabel(self.ylabel)
self.axes.spines['right'].set_color('none')
self.axes.spines['top'].set_color('none')
'''
self.axes.set_xticks([30,40,50,60,70,80,90])
self.axes.set_xticklabels([30,40,50,60,70,80,90])
self.axes.set_yticks([0, 5, 10, 15, 20])
self.axes.set_yticklabels([0, 5, 10, 15, 20])
self.axes.set_ylim(bottom=0)
'''
all_labels=[]
all_colors=[]
all_markers=[]
all_alpha=[]
for i in range(len(self._df)):
target = self._df.at[i, 'Label']
color = self._df.at[i, 'Color']
marker = self._df.at[i, 'Marker']
alpha = self._df.at[i, 'Alpha']
if target not in self.SVM_labels:
self.SVM_labels.append(target)
if target not in all_labels:
all_labels.append(target)
all_colors.append(color)
all_markers.append(marker)
all_alpha.append(alpha)
self.whole_labels = all_labels
PointLabels = []
PointColors = []
x = []
y = []
title = 'K2O-SiO2diagram'
self.setWindowTitle(title)
self.textbox.setText(self.reference)
k_1=(2.9-1.2)/(68-48)
y_1= 1.2+ (85-48)*k_1
y_0= 1.2+ (45-48)*k_1
self.DrawLine([(45, y_0),(48, 1.2), (68,2.9),(85,y_1)])
k_2=(1.2-0.3)/(68-48)
y_2= 0.3+ (85-48)*k_2
y_3= 0.3+ (45-48)*k_2
self.DrawLine([(45, y_3),(48, 0.3), (68, 1.2),(85,y_2)])
Labels=['High K','Medium K','Low K']
Locations=[(80,5),(80,3),(80,1)]
X_offset, Y_offset=0,0
for k in range(len(Labels)):
self.axes.annotate(Labels[k], Locations[k], xycoords='data', xytext=(X_offset, Y_offset),
textcoords='offset points',
fontsize=9, color='grey', alpha=0.8)
self.Check()
if self.OutPutCheck==True:
pass
if (self._changed):
df = self.CleanDataFile(self._df)
for i in range(len(df)):
TmpLabel = ''
if (df.at[i, 'Label'] in PointLabels or df.at[i, 'Label'] == ''):
TmpLabel = ''
else:
PointLabels.append(df.at[i, 'Label'])
TmpLabel = df.at[i, 'Label']
TmpColor = ''
if (df.at[i, 'Color'] in PointColors or df.at[i, 'Color'] == ''):
TmpColor = ''
else:
PointColors.append(df.at[i, 'Color'])
TmpColor = df.at[i, 'Color']
x.append(df.at[i, 'SiO2'])
y.append(df.at[i, 'K2O'])
Size = df.at[i, 'Size']
Color = df.at[i, 'Color']
# print(Color, df.at[i, 'SiO2'], (df.at[i, 'Na2O'] + df.at[i, 'K2O']))
Alpha = df.at[i, 'Alpha']
Marker = df.at[i, 'Marker']
Label = df.at[i, 'Label']
xtest=df.at[i, 'SiO2']
ytest=df.at[i, 'K2O']
for j in self.ItemNames:
if self.SelectDic[j].contains_point([xtest,ytest]):
self.LabelList.append(Label)
self.TypeList.append(j)
break
pass
self.axes.scatter(df.at[i, 'SiO2'], df.at[i, 'K2O'], marker=df.at[i, 'Marker'],
s=df.at[i, 'Size'], color=df.at[i, 'Color'], alpha=df.at[i, 'Alpha'], label=TmpLabel)
XtoFit = {}
YtoFit = {}
SVM_X=[]
SVM_Y=[]
for i in PointLabels:
XtoFit[i]=[]
YtoFit[i]=[]
for i in range(len(df)):
Alpha = df.at[i, 'Alpha']
Marker = df.at[i, 'Marker']
Label = df.at[i, 'Label']
xtest=df.at[i, 'SiO2']
ytest=df.at[i, 'K2O']
XtoFit[Label].append(xtest)
YtoFit[Label].append(ytest)
SVM_X.append(xtest)
SVM_Y.append(ytest)
if (self.shape_cb.isChecked()):
for i in PointLabels:
if XtoFit[i] != YtoFit[i]:
xmin, xmax = min(XtoFit[i]), max(XtoFit[i])
ymin, ymax = min(YtoFit[i]), max(YtoFit[i])
DensityColorMap = 'Greys'
DensityAlpha = 0.1
DensityLineColor = PointColors[PointLabels.index(i)]
DensityLineAlpha = 0.3
# Peform the kernel density estimate
xx, yy = np.mgrid[xmin:xmax:200j, ymin:ymax:200j]
# print(self.ShapeGroups)
# command='''xx, yy = np.mgrid[xmin:xmax:'''+str(self.ShapeGroups)+ '''j, ymin:ymax:''' +str(self.ShapeGroups)+'''j]'''
# exec(command)
# print(xx, yy)
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([XtoFit[i], YtoFit[i]])
kernelstatus = True
try:
st.gaussian_kde(values)
except Exception as e:
self.ErrorEvent(text=repr(e))
kernelstatus = False
if kernelstatus == True:
kernel = st.gaussian_kde(values)
f = np.reshape(kernel(positions).T, xx.shape)
# Contourf plot
cfset = self.axes.contourf(xx, yy, f, cmap=DensityColorMap, alpha=DensityAlpha)
## Or kernel density estimate plot instead of the contourf plot
# self.axes.imshow(np.rot90(f), cmap='Blues', extent=[xmin, xmax, ymin, ymax])
# Contour plot
cset = self.axes.contour(xx, yy, f, colors=DensityLineColor, alpha=DensityLineAlpha)
# Label plot
#self.axes.clabel(cset, inline=1, fontsize=10)
if (len(self.data_to_test) > 0):
contained = True
missing = 'Miss setting infor:'
for i in ['Label', 'Color', 'Marker', 'Alpha']:
if i not in self.data_to_test.columns.values.tolist():
contained = False
missing = missing + '\n' + i
if contained == True:
for i in self.data_to_test.columns.values.tolist():
if i not in self._df.columns.values.tolist():
self.data_to_test = self.data_to_test.drop(columns=i)
# print(self.data_to_test)
test_labels = []
test_colors = []
test_markers = []
test_alpha = []
for i in range(len(self.data_to_test)):
# print(self.data_to_test.at[i, 'Label'])
target = self.data_to_test.at[i, 'Label']
color = self.data_to_test.at[i, 'Color']
marker = self.data_to_test.at[i, 'Marker']
alpha = self.data_to_test.at[i, 'Alpha']
if target not in test_labels and target not in all_labels:
test_labels.append(target)
test_colors.append(color)
test_markers.append(marker)
test_alpha.append(alpha)
self.whole_labels = self.whole_labels + test_labels
self.load_settings_backup = self.data_to_test
Load_ItemsToTest = ['Label', 'Number', 'Tag', 'Name', 'Author', 'DataType', 'Marker', 'Color',
'Size',
'Alpha',
'Style', 'Width']
for i in self.data_to_test.columns.values.tolist():
if i not in Load_ItemsToTest:
self.load_settings_backup = self.load_settings_backup.drop(i, 1)
print(self.load_settings_backup, self.data_to_test)
print(self.load_settings_backup.shape, self.data_to_test.shape)
try:
for i in range(len(self.data_to_test)):
target = self.data_to_test.at[i, 'Label']
if target not in all_labels:
all_labels.append(target)
tmp_label = self.data_to_test.at[i, 'Label']
else:
tmp_label=''
x_load_test = self.data_to_test.at[i, 'SiO2']
y_load_test = self.data_to_test.at[i, 'K2O']
for j in self.ItemNames:
if self.SelectDic[j].contains_point([x_load_test, y_load_test]):
self.LabelList.append(self.data_to_test.at[i, 'Label'])
self.TypeList.append(j)
break
pass
if (self.show_load_data_cb.isChecked()):
self.axes.scatter(self.data_to_test.at[i, 'SiO2'],self.data_to_test.at[i, 'K2O'],
marker=self.data_to_test.at[i, 'Marker'],
s=self.data_to_test.at[i, 'Size'],
color=self.data_to_test.at[i, 'Color'],
alpha=self.data_to_test.at[i, 'Alpha'],
label=tmp_label)
except Exception as e:
self.ErrorEvent(text=repr(e))
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0, prop=fontprop)
self.All_X=SVM_X
self.All_Y=SVM_Y
if (self.hyperplane_cb.isChecked()):
clf = svm.SVC(C=1.0, kernel='linear',probability= True)
svm_x= SVM_X
svm_y= SVM_Y
print(len(svm_x),len(svm_y),len(df.index))
xx, yy = np.meshgrid(np.arange(min(svm_x), max(svm_x), np.ptp(svm_x) / 500),
np.arange(min(svm_y), max(svm_y), np.ptp(svm_y) / 500))
le = LabelEncoder()
le.fit(self._df.Label)
class_label = le.transform(self._df.Label)
svm_train= pd.concat([pd.DataFrame(svm_x),pd.DataFrame(svm_y)], axis=1)
svm_train=svm_train.values
clf.fit(svm_train,class_label)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
self.axes.contourf(xx, yy, Z, cmap='hot', alpha=0.2)
if (self.show_data_index_cb.isChecked()):
if 'Index' in self._df.columns.values:
for i in range(len(self._df)):
self.axes.annotate(self._df.at[i, 'Index'],
xy=(self.All_X[i],
self.All_Y[i]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
else:
for i in range(len(self._df)):
self.axes.annotate('No' + str(i + 1),
xy=(self.All_X[i],
self.All_Y[i]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
self.canvas.draw()
self.OutPutTitle='K2OSiO2'
self.OutPutData = pd.DataFrame(
{'Label': self.LabelList,
'RockType': self.TypeList
})
self.OutPutFig=self.fig | python | def K2OSiO2(self, Left=35, Right=79, X0=30, X1=90, X_Gap=7, Base=0,
Top=19, Y0=1, Y1=19, Y_Gap=19, FontSize=12, xlabel=r'$SiO_2 wt\%$', ylabel=r'$K_2O wt\%$', width=12,
height=12, dpi=300):
self.setWindowTitle('K2OSiO2 diagram ')
self.axes.clear()
self.axes.set_xlabel(self.xlabel)
self.axes.set_ylabel(self.ylabel)
self.axes.spines['right'].set_color('none')
self.axes.spines['top'].set_color('none')
all_labels=[]
all_colors=[]
all_markers=[]
all_alpha=[]
for i in range(len(self._df)):
target = self._df.at[i, 'Label']
color = self._df.at[i, 'Color']
marker = self._df.at[i, 'Marker']
alpha = self._df.at[i, 'Alpha']
if target not in self.SVM_labels:
self.SVM_labels.append(target)
if target not in all_labels:
all_labels.append(target)
all_colors.append(color)
all_markers.append(marker)
all_alpha.append(alpha)
self.whole_labels = all_labels
PointLabels = []
PointColors = []
x = []
y = []
title = 'K2O-SiO2diagram'
self.setWindowTitle(title)
self.textbox.setText(self.reference)
k_1=(2.9-1.2)/(68-48)
y_1= 1.2+ (85-48)*k_1
y_0= 1.2+ (45-48)*k_1
self.DrawLine([(45, y_0),(48, 1.2), (68,2.9),(85,y_1)])
k_2=(1.2-0.3)/(68-48)
y_2= 0.3+ (85-48)*k_2
y_3= 0.3+ (45-48)*k_2
self.DrawLine([(45, y_3),(48, 0.3), (68, 1.2),(85,y_2)])
Labels=['High K','Medium K','Low K']
Locations=[(80,5),(80,3),(80,1)]
X_offset, Y_offset=0,0
for k in range(len(Labels)):
self.axes.annotate(Labels[k], Locations[k], xycoords='data', xytext=(X_offset, Y_offset),
textcoords='offset points',
fontsize=9, color='grey', alpha=0.8)
self.Check()
if self.OutPutCheck==True:
pass
if (self._changed):
df = self.CleanDataFile(self._df)
for i in range(len(df)):
TmpLabel = ''
if (df.at[i, 'Label'] in PointLabels or df.at[i, 'Label'] == ''):
TmpLabel = ''
else:
PointLabels.append(df.at[i, 'Label'])
TmpLabel = df.at[i, 'Label']
TmpColor = ''
if (df.at[i, 'Color'] in PointColors or df.at[i, 'Color'] == ''):
TmpColor = ''
else:
PointColors.append(df.at[i, 'Color'])
TmpColor = df.at[i, 'Color']
x.append(df.at[i, 'SiO2'])
y.append(df.at[i, 'K2O'])
Size = df.at[i, 'Size']
Color = df.at[i, 'Color']
Alpha = df.at[i, 'Alpha']
Marker = df.at[i, 'Marker']
Label = df.at[i, 'Label']
xtest=df.at[i, 'SiO2']
ytest=df.at[i, 'K2O']
for j in self.ItemNames:
if self.SelectDic[j].contains_point([xtest,ytest]):
self.LabelList.append(Label)
self.TypeList.append(j)
break
pass
self.axes.scatter(df.at[i, 'SiO2'], df.at[i, 'K2O'], marker=df.at[i, 'Marker'],
s=df.at[i, 'Size'], color=df.at[i, 'Color'], alpha=df.at[i, 'Alpha'], label=TmpLabel)
XtoFit = {}
YtoFit = {}
SVM_X=[]
SVM_Y=[]
for i in PointLabels:
XtoFit[i]=[]
YtoFit[i]=[]
for i in range(len(df)):
Alpha = df.at[i, 'Alpha']
Marker = df.at[i, 'Marker']
Label = df.at[i, 'Label']
xtest=df.at[i, 'SiO2']
ytest=df.at[i, 'K2O']
XtoFit[Label].append(xtest)
YtoFit[Label].append(ytest)
SVM_X.append(xtest)
SVM_Y.append(ytest)
if (self.shape_cb.isChecked()):
for i in PointLabels:
if XtoFit[i] != YtoFit[i]:
xmin, xmax = min(XtoFit[i]), max(XtoFit[i])
ymin, ymax = min(YtoFit[i]), max(YtoFit[i])
DensityColorMap = 'Greys'
DensityAlpha = 0.1
DensityLineColor = PointColors[PointLabels.index(i)]
DensityLineAlpha = 0.3
xx, yy = np.mgrid[xmin:xmax:200j, ymin:ymax:200j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([XtoFit[i], YtoFit[i]])
kernelstatus = True
try:
st.gaussian_kde(values)
except Exception as e:
self.ErrorEvent(text=repr(e))
kernelstatus = False
if kernelstatus == True:
kernel = st.gaussian_kde(values)
f = np.reshape(kernel(positions).T, xx.shape)
cfset = self.axes.contourf(xx, yy, f, cmap=DensityColorMap, alpha=DensityAlpha)
cset = self.axes.contour(xx, yy, f, colors=DensityLineColor, alpha=DensityLineAlpha)
if (len(self.data_to_test) > 0):
contained = True
missing = 'Miss setting infor:'
for i in ['Label', 'Color', 'Marker', 'Alpha']:
if i not in self.data_to_test.columns.values.tolist():
contained = False
missing = missing + '\n' + i
if contained == True:
for i in self.data_to_test.columns.values.tolist():
if i not in self._df.columns.values.tolist():
self.data_to_test = self.data_to_test.drop(columns=i)
test_labels = []
test_colors = []
test_markers = []
test_alpha = []
for i in range(len(self.data_to_test)):
target = self.data_to_test.at[i, 'Label']
color = self.data_to_test.at[i, 'Color']
marker = self.data_to_test.at[i, 'Marker']
alpha = self.data_to_test.at[i, 'Alpha']
if target not in test_labels and target not in all_labels:
test_labels.append(target)
test_colors.append(color)
test_markers.append(marker)
test_alpha.append(alpha)
self.whole_labels = self.whole_labels + test_labels
self.load_settings_backup = self.data_to_test
Load_ItemsToTest = ['Label', 'Number', 'Tag', 'Name', 'Author', 'DataType', 'Marker', 'Color',
'Size',
'Alpha',
'Style', 'Width']
for i in self.data_to_test.columns.values.tolist():
if i not in Load_ItemsToTest:
self.load_settings_backup = self.load_settings_backup.drop(i, 1)
print(self.load_settings_backup, self.data_to_test)
print(self.load_settings_backup.shape, self.data_to_test.shape)
try:
for i in range(len(self.data_to_test)):
target = self.data_to_test.at[i, 'Label']
if target not in all_labels:
all_labels.append(target)
tmp_label = self.data_to_test.at[i, 'Label']
else:
tmp_label=''
x_load_test = self.data_to_test.at[i, 'SiO2']
y_load_test = self.data_to_test.at[i, 'K2O']
for j in self.ItemNames:
if self.SelectDic[j].contains_point([x_load_test, y_load_test]):
self.LabelList.append(self.data_to_test.at[i, 'Label'])
self.TypeList.append(j)
break
pass
if (self.show_load_data_cb.isChecked()):
self.axes.scatter(self.data_to_test.at[i, 'SiO2'],self.data_to_test.at[i, 'K2O'],
marker=self.data_to_test.at[i, 'Marker'],
s=self.data_to_test.at[i, 'Size'],
color=self.data_to_test.at[i, 'Color'],
alpha=self.data_to_test.at[i, 'Alpha'],
label=tmp_label)
except Exception as e:
self.ErrorEvent(text=repr(e))
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0, prop=fontprop)
self.All_X=SVM_X
self.All_Y=SVM_Y
if (self.hyperplane_cb.isChecked()):
clf = svm.SVC(C=1.0, kernel='linear',probability= True)
svm_x= SVM_X
svm_y= SVM_Y
print(len(svm_x),len(svm_y),len(df.index))
xx, yy = np.meshgrid(np.arange(min(svm_x), max(svm_x), np.ptp(svm_x) / 500),
np.arange(min(svm_y), max(svm_y), np.ptp(svm_y) / 500))
le = LabelEncoder()
le.fit(self._df.Label)
class_label = le.transform(self._df.Label)
svm_train= pd.concat([pd.DataFrame(svm_x),pd.DataFrame(svm_y)], axis=1)
svm_train=svm_train.values
clf.fit(svm_train,class_label)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
self.axes.contourf(xx, yy, Z, cmap='hot', alpha=0.2)
if (self.show_data_index_cb.isChecked()):
if 'Index' in self._df.columns.values:
for i in range(len(self._df)):
self.axes.annotate(self._df.at[i, 'Index'],
xy=(self.All_X[i],
self.All_Y[i]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
else:
for i in range(len(self._df)):
self.axes.annotate('No' + str(i + 1),
xy=(self.All_X[i],
self.All_Y[i]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
self.canvas.draw()
self.OutPutTitle='K2OSiO2'
self.OutPutData = pd.DataFrame(
{'Label': self.LabelList,
'RockType': self.TypeList
})
self.OutPutFig=self.fig | [
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| self.axes.set_xticks([30,40,50,60,70,80,90])
self.axes.set_xticklabels([30,40,50,60,70,80,90])
self.axes.set_yticks([0, 5, 10, 15, 20])
self.axes.set_yticklabels([0, 5, 10, 15, 20])
self.axes.set_ylim(bottom=0) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/K2OSiO2.py#L122-L471 |
GeoPyTool/GeoPyTool | geopytool/REE.py | REE.REE | def REE(self, Left=0, Right=16, X0=1, X1=15, X_Gap=15, Base=-1,
Top=6, Y0=-1,
Y1=3, Y_Gap=5, FontSize=12,
xLabel=r'$REE-Standardlized-Pattern$', yLabel='', width=12, height=12, dpi=300):
self.axes.clear()
self.axes.spines['right'].set_color('none')
self.axes.spines['top'].set_color('none')
self.WholeData = []
self.OutPutData = pd.DataFrame()
self.LabelList=[]
self.algebraDeltaEuList=[]
self.geometricDeltaEuList=[]
self.LaPrDeltaCeList=[]
self.LaNdDeltaCeList=[]
self.LaSmList=[]
self.LaYbList=[]
self.GdYbList=[]
self.LREEList=[]
self.MREEList=[]
self.HREEList=[]
self.ALLREEList=[]
self.BinHREEList=[]
self.BinLREEList=[]
self.L_HREEList=[]
self.AllAlpha=[]
self.AllWidth = []
self.AllSize = []
#raw = self._df
raw = self.CleanDataFile(self._df)
self.FontSize = FontSize
PointLabels = []
k = 0
slider_value=int(self.standard_slider.value())
item_value=int(self.item_slider.value())
if slider_value < len(self.StandardsName):
standardnamechosen = self.StandardsName[slider_value]
standardchosen = self.Standards[standardnamechosen]
self.textbox.setText(self.reference+"\nStandard Chosen: "+self.StandardsName[slider_value])
right_label_text=self.StandardsName[slider_value]
elif len(self._given_Standard)<=0:
standardnamechosen = self.StandardsName[slider_value-1]
standardchosen = self.Standards[standardnamechosen]
self.textbox.setText(self.reference+"\nStandard Chosen: "+self.StandardsName[slider_value-1])
right_label_text = self.StandardsName[slider_value-1]
else:
standardchosen = self._given_Standard
self.textbox.setText(self.reference + "\n You are using Self Defined Standard")
right_label_text = "Self Defined Standard"
for i in range(len(raw)):
# raw.at[i, 'DataType'] == 'User' or raw.at[i, 'DataType'] == 'user' or raw.at[i, 'DataType'] == 'USER'
TmpLabel = ''
LinesX = []
LinesY = []
TmpEu = raw.at[i, 'Eu'] / standardchosen['Eu']
TmpSm = raw.at[i, 'Sm'] / standardchosen['Sm']
TmpGd = raw.at[i, 'Gd'] / standardchosen['Gd']
TmpCe = raw.at[i, 'Ce'] / standardchosen['Ce']
TmpLa = raw.at[i, 'La'] / standardchosen['La']
TmpPr = raw.at[i, 'Pr'] / standardchosen['Pr']
TmpNd = raw.at[i, 'Nd'] / standardchosen['Nd']
TmpYb = raw.at[i, 'Yb'] / standardchosen['Yb']
algebraEu = 2*TmpEu/(TmpSm+TmpGd)
geometricEu = TmpEu/np.power((TmpSm*TmpGd),0.5)
firstCe=2*TmpCe/(TmpLa+TmpPr)
secondCe=3*TmpCe/(2*TmpLa+TmpNd)
LaYb=TmpLa/TmpYb
LaSm=TmpLa/TmpSm
GdYb=TmpGd/TmpYb
tmpLREEResult = 0
tmpMREEResult = 0
tmpHREEResult = 0
tmpWholeResult = 0
tmpBinLREE=0
tmpBinHREE=0
for j in self.Element:
if j in self.LREE:
tmpLREEResult += raw.at[i, j]
elif j in self.MREE:
tmpMREEResult += raw.at[i, j]
elif j in self.HREE:
tmpHREEResult += raw.at[i, j]
if j in self.BinLREE:
tmpBinLREE += raw.at[i, j]
elif j in self.BinHREE:
tmpBinHREE += raw.at[i, j]
tmpWholeResult+= raw.at[i, j]
self.LabelList.append(raw.at[i, 'Label'])
self.algebraDeltaEuList.append( algebraEu )
self.geometricDeltaEuList.append( geometricEu )
self.LaPrDeltaCeList.append(firstCe)
self.LaNdDeltaCeList.append(secondCe)
self.LaSmList.append(LaSm)
self.LaYbList.append(LaYb)
self.GdYbList.append(GdYb)
self.LREEList.append( tmpLREEResult )
self.MREEList.append( tmpMREEResult )
self.HREEList.append( tmpHREEResult )
self.ALLREEList.append( tmpWholeResult )
self.BinHREEList.append(tmpBinHREE)
self.BinLREEList.append(tmpBinLREE)
self.L_HREEList.append(tmpBinLREE/tmpBinHREE)
'''
for i in self.data_to_norm.columns.values.tolist():
if i not in self.Element:
self.data_to_norm = self.data_to_norm.drop(i, 1)
'''
Y_bottom=0
Y_top=0
for j in range(len(self.Element)):
tmp = raw.at[i, self.Element[j]] / standardchosen[self.Element[j]]
self.data_to_norm.at[i, self.Element[j]] = tmp
tmpflag = 1
a = 0
try:
a = math.log(tmp, 10)
except(ValueError):
tmpflag = 0
pass
if (tmpflag == 1):
if Y_bottom >a:Y_bottom =a
if Y_top<a:Y_top=a
self.axes.set_ylim(Y_bottom, Y_top+1)
if item_value == 0:
self.item_left_label.setText('Show All')
for j in range(len(self.Element)):
tmp = raw.at[i, self.Element[j]] / standardchosen[self.Element[j]]
self.data_to_norm.at[i, self.Element[j]] = tmp
tmpflag = 1
a = 0
try:
a = math.log(tmp, 10)
except(ValueError):
tmpflag = 0
pass
if (tmpflag == 1):
if (raw.at[i, 'Label'] in PointLabels or raw.at[i, 'Label'] == ''):
self.WholeData.append(math.log(tmp, 10))
TmpLabel = ''
else:
PointLabels.append(raw.at[i, 'Label'])
TmpLabel = raw.at[i, 'Label']
LinesY.append(a)
LinesX.append(j + 1)
self.axes.scatter(j + 1, math.log(tmp, 10), marker=raw.at[i, 'Marker'],
s=raw.at[i, 'Size'], color=raw.at[i, 'Color'], alpha=raw.at[i, 'Alpha'],
label=TmpLabel)
self.axes.plot(LinesX, LinesY, color=raw.at[i, 'Color'], linewidth=raw.at[i, 'Width'],
linestyle=raw.at[i, 'Style'], alpha=raw.at[i, 'Alpha'])
if (self.show_data_index_cb.isChecked()):
if 'Index' in self._df_back.columns.values:
self.axes.annotate(self._df_back.at[i, 'Index'],
xy=(LinesX[-1], LinesY[-1]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
else:
self.axes.annotate('No' + str(i + 1),
xy=(LinesX[-1],
LinesY[-1]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
else:
self.item_left_label.setText(self.AllLabel[item_value-1])
for j in range(len(self.Element)):
tmp = raw.at[i, self.Element[j]] / standardchosen[self.Element[j]]
self.data_to_norm.at[i, self.Element[j]] = tmp
tmpflag = 1
a = 0
try:
a = math.log(tmp, 10)
except(ValueError):
tmpflag = 0
pass
if (tmpflag == 1):
if (raw.at[i, 'Label'] in PointLabels or raw.at[i, 'Label'] == ''):
TmpLabel = ''
else:
PointLabels.append(raw.at[i, 'Label'])
TmpLabel = raw.at[i, 'Label']
alpha= raw.at[i, 'Alpha']
linewidth= raw.at[i, 'Width']
pointsize= raw.at[i, 'Size']
if raw.at[i, 'Label'] == self.AllLabel[item_value - 1]:
LinesY.append(a)
LinesX.append(j + 1)
self.WholeData.append(math.log(tmp, 10))
self.axes.scatter(j + 1, math.log(tmp, 10), marker=raw.at[i, 'Marker'],
s=pointsize, color=raw.at[i, 'Color'], alpha=alpha,
label=TmpLabel)
self.axes.plot(LinesX, LinesY, color=raw.at[i, 'Color'], linewidth=linewidth,linestyle=raw.at[i, 'Style'], alpha=alpha)
print(LinesX,LinesY)
if (self.show_data_index_cb.isChecked()):
if len(LinesX) > 0 and len(LinesY) > 0:
if 'Index' in self._df_back.columns.values:
self.axes.annotate(self._df_back.at[i, 'Index'],
xy=(LinesX[-1], LinesY[-1]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
else:
self.axes.annotate('No' + str(i + 1),
xy=(LinesX[-1], LinesY[-1]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
Tale = 0
Head = 0
if (len(self.WholeData) > 0):
Tale = min(self.WholeData)
Head = max(self.WholeData)+0.5
Location = round(Tale - (Head - Tale) / 5)
count = round((Head - Tale) / 5 * 7) + 1
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0, prop=fontprop)
self.standard_right_label.setText(right_label_text)
self.yticks = [Location+i for i in range(count)]
self.yticklabels = [str(np.power(10.0, (Location + i))) for i in range(count)]
self.axes.set_yticks(self.yticks)
self.axes.set_yticklabels(self.yticklabels, fontsize=6)
#self.axes.set_yscale('log')
self.axes.set_xticks(self.xticks)
self.axes.set_xticklabels(self.xticklabels, rotation=-45, fontsize=6)
self.canvas.draw()
self.OutPutTitle='REE'
#print(len(self.algebraDeltaEuList))
self.OutPutData = pd.DataFrame(
{'Label': self.LabelList,
'Eu/Eu*(algebra)': self.algebraDeltaEuList,
'Eu/Eu*(square)': self.geometricDeltaEuList,
'Ce/Ce*(LaPr)': self.LaPrDeltaCeList,
'Ce/Ce*(LaNd)': self.LaNdDeltaCeList,
'(La/Sm)N':self.LaSmList,
'(La/Yb)N':self.LaYbList,
'(Gd/Yb)N':self.GdYbList,
'trisection LREE': self.LREEList,
'trisection MREE': self.MREEList,
'trisection HREE': self.HREEList,
'bisection LREE': self.BinLREEList,
'bisection HREE': self.BinHREEList,
'LREE/HREE':self.L_HREEList,
'ALLREE': self.ALLREEList
})
#print('middle ',len(self.OutPutData['Eu/Eu*(algebra)']))
'''
self.LaPrDeltaCeList.append(firstCe)
self.LaNdDeltaCeList.append(secondCe)
'''
self.OutPutFig=self.fig | python | def REE(self, Left=0, Right=16, X0=1, X1=15, X_Gap=15, Base=-1,
Top=6, Y0=-1,
Y1=3, Y_Gap=5, FontSize=12,
xLabel=r'$REE-Standardlized-Pattern$', yLabel='', width=12, height=12, dpi=300):
self.axes.clear()
self.axes.spines['right'].set_color('none')
self.axes.spines['top'].set_color('none')
self.WholeData = []
self.OutPutData = pd.DataFrame()
self.LabelList=[]
self.algebraDeltaEuList=[]
self.geometricDeltaEuList=[]
self.LaPrDeltaCeList=[]
self.LaNdDeltaCeList=[]
self.LaSmList=[]
self.LaYbList=[]
self.GdYbList=[]
self.LREEList=[]
self.MREEList=[]
self.HREEList=[]
self.ALLREEList=[]
self.BinHREEList=[]
self.BinLREEList=[]
self.L_HREEList=[]
self.AllAlpha=[]
self.AllWidth = []
self.AllSize = []
raw = self.CleanDataFile(self._df)
self.FontSize = FontSize
PointLabels = []
k = 0
slider_value=int(self.standard_slider.value())
item_value=int(self.item_slider.value())
if slider_value < len(self.StandardsName):
standardnamechosen = self.StandardsName[slider_value]
standardchosen = self.Standards[standardnamechosen]
self.textbox.setText(self.reference+"\nStandard Chosen: "+self.StandardsName[slider_value])
right_label_text=self.StandardsName[slider_value]
elif len(self._given_Standard)<=0:
standardnamechosen = self.StandardsName[slider_value-1]
standardchosen = self.Standards[standardnamechosen]
self.textbox.setText(self.reference+"\nStandard Chosen: "+self.StandardsName[slider_value-1])
right_label_text = self.StandardsName[slider_value-1]
else:
standardchosen = self._given_Standard
self.textbox.setText(self.reference + "\n You are using Self Defined Standard")
right_label_text = "Self Defined Standard"
for i in range(len(raw)):
TmpLabel = ''
LinesX = []
LinesY = []
TmpEu = raw.at[i, 'Eu'] / standardchosen['Eu']
TmpSm = raw.at[i, 'Sm'] / standardchosen['Sm']
TmpGd = raw.at[i, 'Gd'] / standardchosen['Gd']
TmpCe = raw.at[i, 'Ce'] / standardchosen['Ce']
TmpLa = raw.at[i, 'La'] / standardchosen['La']
TmpPr = raw.at[i, 'Pr'] / standardchosen['Pr']
TmpNd = raw.at[i, 'Nd'] / standardchosen['Nd']
TmpYb = raw.at[i, 'Yb'] / standardchosen['Yb']
algebraEu = 2*TmpEu/(TmpSm+TmpGd)
geometricEu = TmpEu/np.power((TmpSm*TmpGd),0.5)
firstCe=2*TmpCe/(TmpLa+TmpPr)
secondCe=3*TmpCe/(2*TmpLa+TmpNd)
LaYb=TmpLa/TmpYb
LaSm=TmpLa/TmpSm
GdYb=TmpGd/TmpYb
tmpLREEResult = 0
tmpMREEResult = 0
tmpHREEResult = 0
tmpWholeResult = 0
tmpBinLREE=0
tmpBinHREE=0
for j in self.Element:
if j in self.LREE:
tmpLREEResult += raw.at[i, j]
elif j in self.MREE:
tmpMREEResult += raw.at[i, j]
elif j in self.HREE:
tmpHREEResult += raw.at[i, j]
if j in self.BinLREE:
tmpBinLREE += raw.at[i, j]
elif j in self.BinHREE:
tmpBinHREE += raw.at[i, j]
tmpWholeResult+= raw.at[i, j]
self.LabelList.append(raw.at[i, 'Label'])
self.algebraDeltaEuList.append( algebraEu )
self.geometricDeltaEuList.append( geometricEu )
self.LaPrDeltaCeList.append(firstCe)
self.LaNdDeltaCeList.append(secondCe)
self.LaSmList.append(LaSm)
self.LaYbList.append(LaYb)
self.GdYbList.append(GdYb)
self.LREEList.append( tmpLREEResult )
self.MREEList.append( tmpMREEResult )
self.HREEList.append( tmpHREEResult )
self.ALLREEList.append( tmpWholeResult )
self.BinHREEList.append(tmpBinHREE)
self.BinLREEList.append(tmpBinLREE)
self.L_HREEList.append(tmpBinLREE/tmpBinHREE)
Y_bottom=0
Y_top=0
for j in range(len(self.Element)):
tmp = raw.at[i, self.Element[j]] / standardchosen[self.Element[j]]
self.data_to_norm.at[i, self.Element[j]] = tmp
tmpflag = 1
a = 0
try:
a = math.log(tmp, 10)
except(ValueError):
tmpflag = 0
pass
if (tmpflag == 1):
if Y_bottom >a:Y_bottom =a
if Y_top<a:Y_top=a
self.axes.set_ylim(Y_bottom, Y_top+1)
if item_value == 0:
self.item_left_label.setText('Show All')
for j in range(len(self.Element)):
tmp = raw.at[i, self.Element[j]] / standardchosen[self.Element[j]]
self.data_to_norm.at[i, self.Element[j]] = tmp
tmpflag = 1
a = 0
try:
a = math.log(tmp, 10)
except(ValueError):
tmpflag = 0
pass
if (tmpflag == 1):
if (raw.at[i, 'Label'] in PointLabels or raw.at[i, 'Label'] == ''):
self.WholeData.append(math.log(tmp, 10))
TmpLabel = ''
else:
PointLabels.append(raw.at[i, 'Label'])
TmpLabel = raw.at[i, 'Label']
LinesY.append(a)
LinesX.append(j + 1)
self.axes.scatter(j + 1, math.log(tmp, 10), marker=raw.at[i, 'Marker'],
s=raw.at[i, 'Size'], color=raw.at[i, 'Color'], alpha=raw.at[i, 'Alpha'],
label=TmpLabel)
self.axes.plot(LinesX, LinesY, color=raw.at[i, 'Color'], linewidth=raw.at[i, 'Width'],
linestyle=raw.at[i, 'Style'], alpha=raw.at[i, 'Alpha'])
if (self.show_data_index_cb.isChecked()):
if 'Index' in self._df_back.columns.values:
self.axes.annotate(self._df_back.at[i, 'Index'],
xy=(LinesX[-1], LinesY[-1]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
else:
self.axes.annotate('No' + str(i + 1),
xy=(LinesX[-1],
LinesY[-1]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
else:
self.item_left_label.setText(self.AllLabel[item_value-1])
for j in range(len(self.Element)):
tmp = raw.at[i, self.Element[j]] / standardchosen[self.Element[j]]
self.data_to_norm.at[i, self.Element[j]] = tmp
tmpflag = 1
a = 0
try:
a = math.log(tmp, 10)
except(ValueError):
tmpflag = 0
pass
if (tmpflag == 1):
if (raw.at[i, 'Label'] in PointLabels or raw.at[i, 'Label'] == ''):
TmpLabel = ''
else:
PointLabels.append(raw.at[i, 'Label'])
TmpLabel = raw.at[i, 'Label']
alpha= raw.at[i, 'Alpha']
linewidth= raw.at[i, 'Width']
pointsize= raw.at[i, 'Size']
if raw.at[i, 'Label'] == self.AllLabel[item_value - 1]:
LinesY.append(a)
LinesX.append(j + 1)
self.WholeData.append(math.log(tmp, 10))
self.axes.scatter(j + 1, math.log(tmp, 10), marker=raw.at[i, 'Marker'],
s=pointsize, color=raw.at[i, 'Color'], alpha=alpha,
label=TmpLabel)
self.axes.plot(LinesX, LinesY, color=raw.at[i, 'Color'], linewidth=linewidth,linestyle=raw.at[i, 'Style'], alpha=alpha)
print(LinesX,LinesY)
if (self.show_data_index_cb.isChecked()):
if len(LinesX) > 0 and len(LinesY) > 0:
if 'Index' in self._df_back.columns.values:
self.axes.annotate(self._df_back.at[i, 'Index'],
xy=(LinesX[-1], LinesY[-1]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
else:
self.axes.annotate('No' + str(i + 1),
xy=(LinesX[-1], LinesY[-1]),
color=self._df.at[i, 'Color'],
alpha=self._df.at[i, 'Alpha'])
Tale = 0
Head = 0
if (len(self.WholeData) > 0):
Tale = min(self.WholeData)
Head = max(self.WholeData)+0.5
Location = round(Tale - (Head - Tale) / 5)
count = round((Head - Tale) / 5 * 7) + 1
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0, prop=fontprop)
self.standard_right_label.setText(right_label_text)
self.yticks = [Location+i for i in range(count)]
self.yticklabels = [str(np.power(10.0, (Location + i))) for i in range(count)]
self.axes.set_yticks(self.yticks)
self.axes.set_yticklabels(self.yticklabels, fontsize=6)
self.axes.set_xticks(self.xticks)
self.axes.set_xticklabels(self.xticklabels, rotation=-45, fontsize=6)
self.canvas.draw()
self.OutPutTitle='REE'
self.OutPutData = pd.DataFrame(
{'Label': self.LabelList,
'Eu/Eu*(algebra)': self.algebraDeltaEuList,
'Eu/Eu*(square)': self.geometricDeltaEuList,
'Ce/Ce*(LaPr)': self.LaPrDeltaCeList,
'Ce/Ce*(LaNd)': self.LaNdDeltaCeList,
'(La/Sm)N':self.LaSmList,
'(La/Yb)N':self.LaYbList,
'(Gd/Yb)N':self.GdYbList,
'trisection LREE': self.LREEList,
'trisection MREE': self.MREEList,
'trisection HREE': self.HREEList,
'bisection LREE': self.BinLREEList,
'bisection HREE': self.BinHREEList,
'LREE/HREE':self.L_HREEList,
'ALLREE': self.ALLREEList
})
self.OutPutFig=self.fig | [
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| self.LaPrDeltaCeList.append(firstCe)
self.LaNdDeltaCeList.append(secondCe) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/REE.py#L252-L584 |
GeoPyTool/GeoPyTool | geopytool/NewCIPW.py | CIPW.create_main_frame | def create_main_frame(self):
self.resize(800, 600)
self.main_frame = QWidget()
self.dpi = 128
self.save_button = QPushButton('&Save Result')
self.save_button.clicked.connect(self.saveResult)
self.qapf_button = QPushButton('&QAPF')
self.qapf_button.clicked.connect(self.QAPF)
'''
self.tableView = CustomQTableView(self.main_frame)
self.tableView.setObjectName('tableView')
self.tableView.setSortingEnabled(True)
'''
self.tableViewMole = CustomQTableView(self.main_frame)
self.tableViewMole.setObjectName('tableViewMole')
self.tableViewMole.setSortingEnabled(True)
self.tableViewWeight = CustomQTableView(self.main_frame)
self.tableViewWeight.setObjectName('tableViewWeight')
self.tableViewWeight.setSortingEnabled(True)
self.tableViewVolume = CustomQTableView(self.main_frame)
self.tableViewVolume.setObjectName('tableViewVolume')
self.tableViewVolume.setSortingEnabled(True)
self.tableViewCalced = CustomQTableView(self.main_frame)
self.tableViewCalced.setObjectName('tableViewCalced')
self.tableViewCalced.setSortingEnabled(True)
#
# Layout with box sizers
#
self.hbox = QHBoxLayout()
for w in [self.qapf_button]:
self.hbox.addWidget(w)
self.hbox.setAlignment(w, Qt.AlignVCenter)
self.vbox = QVBoxLayout()
self.vbox.addWidget(self.tableViewMole)
self.vbox.addWidget(self.tableViewWeight)
self.vbox.addWidget(self.tableViewVolume)
self.vbox.addWidget(self.tableViewCalced)
self.vbox.addWidget(self.save_button)
self.vbox.addLayout(self.hbox)
self.main_frame.setLayout(self.vbox)
self.setCentralWidget(self.main_frame) | python | def create_main_frame(self):
self.resize(800, 600)
self.main_frame = QWidget()
self.dpi = 128
self.save_button = QPushButton('&Save Result')
self.save_button.clicked.connect(self.saveResult)
self.qapf_button = QPushButton('&QAPF')
self.qapf_button.clicked.connect(self.QAPF)
self.tableViewMole = CustomQTableView(self.main_frame)
self.tableViewMole.setObjectName('tableViewMole')
self.tableViewMole.setSortingEnabled(True)
self.tableViewWeight = CustomQTableView(self.main_frame)
self.tableViewWeight.setObjectName('tableViewWeight')
self.tableViewWeight.setSortingEnabled(True)
self.tableViewVolume = CustomQTableView(self.main_frame)
self.tableViewVolume.setObjectName('tableViewVolume')
self.tableViewVolume.setSortingEnabled(True)
self.tableViewCalced = CustomQTableView(self.main_frame)
self.tableViewCalced.setObjectName('tableViewCalced')
self.tableViewCalced.setSortingEnabled(True)
self.hbox = QHBoxLayout()
for w in [self.qapf_button]:
self.hbox.addWidget(w)
self.hbox.setAlignment(w, Qt.AlignVCenter)
self.vbox = QVBoxLayout()
self.vbox.addWidget(self.tableViewMole)
self.vbox.addWidget(self.tableViewWeight)
self.vbox.addWidget(self.tableViewVolume)
self.vbox.addWidget(self.tableViewCalced)
self.vbox.addWidget(self.save_button)
self.vbox.addLayout(self.hbox)
self.main_frame.setLayout(self.vbox)
self.setCentralWidget(self.main_frame) | [
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| self.tableView = CustomQTableView(self.main_frame)
self.tableView.setObjectName('tableView')
self.tableView.setSortingEnabled(True) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/NewCIPW.py#L162-L221 |
GeoPyTool/GeoPyTool | geopytool/NewCIPW.py | CIPW.singleCalc | def singleCalc(self,
m={'Al2O3': 13.01, 'Alpha': 0.6, 'Ba': 188.0, 'Be': 0.85, 'CaO': 8.35, 'Ce': 28.2, 'Co': 45.2,
'Cr': 117.0, 'Cs': 0.83, 'Cu': 53.5, 'Dy': 5.58, 'Er': 2.96, 'Eu': 1.79, 'Fe2O3': 14.47,
'FeO': 5.51, 'Ga': 19.4, 'Gd': 5.24, 'Hf': 3.38, 'Ho': 1.1, 'K2O': 0.72, 'LOI': 5.05,
'La': 11.4, 'Label': 'ZhangSH2016', 'Li': 15.0, 'Lu': 0.39, 'Mg#': 41.9, 'MgO': 5.26,
'MnO': 0.21, 'Na2O': 1.88, 'Nb': 12.6, 'Nd': 18.4, 'Ni': 69.4, 'P2O5': 0.23, 'Pb': 3.17,
'Pr': 3.95, 'Rb': 18.4, 'Sc': 37.4, 'SiO2': 48.17, 'Size': 10, 'Sm': 5.08, 'Sr': 357,
'Ta': 0.77, 'Tb': 0.88, 'Th': 1.85, 'TiO2': 2.56, 'Tl': 0.06, 'Tm': 0.44, 'Total': 99.91,
'U': 0.41, 'V': 368.0, 'Y': 29.7, 'Yb': 2.68, 'Zn': 100.0, 'Zr': 130.0, }):
DataResult={}
DataWeight={}
DataVolume={}
DataCalced={}
DataResult.update({'Label': m['Label']})
DataWeight.update({'Label': m['Label']})
DataVolume.update({'Label': m['Label']})
DataCalced.update({'Label': m['Label']})
DataResult.update({'Width': m['Width']})
DataWeight.update({'Width': m['Width']})
DataVolume.update({'Width': m['Width']})
DataCalced.update({'Width': m['Width']})
DataResult.update({'Style': m['Style']})
DataWeight.update({'Style': m['Style']})
DataVolume.update({'Style': m['Style']})
DataCalced.update({'Style': m['Style']})
DataResult.update({'Alpha': m['Alpha']})
DataWeight.update({'Alpha': m['Alpha']})
DataVolume.update({'Alpha': m['Alpha']})
DataCalced.update({'Alpha': m['Alpha']})
DataResult.update({'Size': m['Size']})
DataWeight.update({'Size': m['Size']})
DataVolume.update({'Size': m['Size']})
DataCalced.update({'Size': m['Size']})
DataResult.update({'Color': m['Color']})
DataWeight.update({'Color': m['Color']})
DataVolume.update({'Color': m['Color']})
DataCalced.update({'Color': m['Color']})
DataResult.update({'Marker': m['Marker']})
DataWeight.update({'Marker': m['Marker']})
DataVolume.update({'Marker': m['Marker']})
DataCalced.update({'Marker': m['Marker']})
WholeMass = 0
EachMole = {}
for j in self.Elements:
'''
Get the Whole Mole of the dataset
'''
try:
T_TMP = m[j]
except(KeyError):
T_TMP = 0
if j == 'Sr':
TMP = T_TMP / (87.62 / 103.619 * 10000)
elif j == 'Ba':
TMP = T_TMP / (137.327 / 153.326 * 10000)
elif j == 'Ni':
TMP = T_TMP / (58.6934 / 74.69239999999999 * 10000)
elif j == 'Cr':
TMP = T_TMP / ((2 * 51.9961) / 151.98919999999998 * 10000)
elif j == 'Zr':
# Zr Multi 2 here
TMP = T_TMP / ((2 * 91.224) / 123.22200000000001 * 10000)
else:
TMP = T_TMP
V = TMP
try:
WholeMass += float(V)
except ValueError:
pass
WeightCorrectionFactor = (100 / WholeMass)
for j in self.Elements:
'''
Get the Mole percentage of each element
'''
try:
T_TMP = m[j]
except(KeyError):
T_TMP = 0
if j == 'Sr':
TMP = T_TMP / (87.62 / 103.619 * 10000)
elif j == 'Ba':
TMP = T_TMP / (137.327 / 153.326 * 10000)
elif j == 'Ni':
TMP = T_TMP / (58.6934 / 74.69239999999999 * 10000)
elif j == 'Cr':
TMP = T_TMP / ((2 * 51.9961) / 151.98919999999998 * 10000)
elif j == 'Zr':
# Zr not Multiple by 2 Here
TMP = T_TMP / ((91.224) / 123.22200000000001 * 10000)
else:
TMP = T_TMP
try:
M = TMP / self.BaseMass[j] * WeightCorrectionFactor
except TypeError:
pass
# M= TMP/NewMass(j) * WeightCorrectionFactor
EachMole.update({j: M})
# self.DataMole.append(EachMole)
DataCalculating = EachMole
Fe3 = DataCalculating['Fe2O3']
Fe2 = DataCalculating['FeO']
Mg = DataCalculating['MgO']
Ca = DataCalculating['CaO']
Na = DataCalculating['Na2O']
try:
DataCalced.update({'Fe3+/(Total Fe) in rock (Mole)': 100 * Fe3 * 2 / (Fe3 * 2 + Fe2)})
except(ZeroDivisionError):
DataCalced.update({'Fe3+/(Total Fe) in rock (Mole)': 0})
pass
try:
DataCalced.update({'Mg/(Mg+Total Fe) in rock (Mole)': 100 * Mg / (Mg + Fe3 * 2 + Fe2)})
except(ZeroDivisionError):
DataCalced.update({'Mg/(Mg+Total Fe) in rock (Mole)': 0})
pass
try:
DataCalced.update({'Mg/(Mg+Fe2+) in rock (Mole)': 100 * Mg / (Mg + Fe2)})
except(ZeroDivisionError):
DataCalced.update({'Mg/(Mg+Fe2+) in rock (Mole)': 0})
pass
try:
DataCalced.update({'Ca/(Ca+Na) in rock (Mole)': 100 * Ca / (Ca + Na * 2)})
except(ZeroDivisionError):
DataCalced.update({'Ca/(Ca+Na) in rock (Mole)': 0})
pass
DataCalculating['CaO'] += DataCalculating['Sr']
DataCalculating['Sr'] = 0
DataCalculating['K2O'] += 2 * DataCalculating['Ba']
DataCalculating['Ba'] = 0
try:
if DataCalculating['CaO'] >= 10 / 3 * DataCalculating['P2O5']:
DataCalculating['CaO'] -= 10 / 3 * DataCalculating['P2O5']
else:
DataCalculating['CaO'] = 0
except(ZeroDivisionError):
pass
DataCalculating['P2O5'] = DataCalculating['P2O5'] / 1.5
Apatite = DataCalculating['P2O5']
# IF(S19>=T15,S19-T15,0)
if DataCalculating['F'] >= DataCalculating['P2O5']:
DataCalculating['F'] -= DataCalculating['P2O5']
else:
DataCalculating['F'] = 0
if DataCalculating['F'] >= DataCalculating['P2O5']:
DataCalculating['F'] -= DataCalculating['P2O5']
else:
DataCalculating['F'] = 0
if DataCalculating['Na2O'] >= DataCalculating['Cl']:
DataCalculating['Na2O'] -= DataCalculating['Cl']
else:
DataCalculating['Na2O'] = 0
Halite = DataCalculating['Cl']
# IF(U12>=(U19/2),U12-(U19/2),0)
if DataCalculating['CaO'] >= 0.5 * DataCalculating['F']:
DataCalculating['CaO'] -= 0.5 * DataCalculating['F']
else:
DataCalculating['CaO'] = 0
DataCalculating['F'] *= 0.5
Fluorite = DataCalculating['F']
# =IF(V17>0,IF(V13>=V17,'Thenardite',IF(V13>0,'Both','Anhydrite')),'None')
AorT = 0
if DataCalculating['SO3'] <= 0:
AorT = 'None'
else:
if DataCalculating['Na2O'] >= DataCalculating['SO3']:
AorT = 'Thenardite'
else:
if DataCalculating['Na2O'] > 0:
AorT = 'Both'
else:
AorT = 'Anhydrite'
# =IF(W26='Anhydrite',V17,IF(W26='Both',V12,0))
# =IF(W26='Thenardite',V17,IF(W26='Both',V17-W17,0))
if AorT == 'Anhydrite':
DataCalculating['Sr'] = 0
elif AorT == 'Thenardite':
DataCalculating['Sr'] = DataCalculating['SO3']
DataCalculating['SO3'] = 0
elif AorT == 'Both':
DataCalculating['Sr'] = DataCalculating['SO3'] - DataCalculating['CaO']
DataCalculating['SO3'] = DataCalculating['CaO']
else:
DataCalculating['SO3'] = 0
DataCalculating['Sr'] = 0
DataCalculating['CaO'] -= DataCalculating['SO3']
DataCalculating['Na2O'] -= DataCalculating['Sr']
Anhydrite = DataCalculating['SO3']
Thenardite = DataCalculating['Sr']
Pyrite = 0.5 * DataCalculating['S']
# =IF(W9>=(W18*0.5),W9-(W18*0.5),0)
if DataCalculating['FeO'] >= DataCalculating['S'] * 0.5:
DataCalculating['FeO'] -= DataCalculating['S'] * 0.5
else:
DataCalculating['FeO'] = 0
# =IF(X24>0,IF(X9>=X24,'Chromite',IF(X9>0,'Both','Magnesiochromite')),'None')
if DataCalculating['Cr'] > 0:
if DataCalculating['FeO'] >= DataCalculating['Cr']:
CorM = 'Chromite'
elif DataCalculating['FeO'] > 0:
CorM = 'Both'
else:
CorM = 'Magnesiochromite'
else:
CorM = 'None'
# =IF(Y26='Chromite',X24,IF(Y26='Both',X9,0))
# =IF(Y26='Magnesiochromite',X24,IF(Y26='Both',X24-Y24,0))
if CorM == 'Chromite':
DataCalculating['Cr'] = DataCalculating['Cr']
DataCalculating['Ni'] = 0
elif CorM == 'Magnesiochromite':
DataCalculating['Ni'] = DataCalculating['Cr']
DataCalculating['Cr'] = 0
elif CorM == 'Both':
DataCalculating['Ni'] = DataCalculating['Cr'] - DataCalculating['FeO']
DataCalculating['Cr'] = DataCalculating['FeO']
else:
DataCalculating['Cr'] = 0
DataCalculating['Ni'] = 0
DataCalculating['MgO'] -= DataCalculating['Ni']
Magnesiochromite = DataCalculating['Ni']
Chromite = DataCalculating['Cr']
# =IF(X9>=Y24,X9-Y24,0)
if DataCalculating['FeO'] >= DataCalculating['Cr']:
DataCalculating['FeO'] -= DataCalculating['Cr']
else:
DataCalculating['FeO'] = 0
# =IF(Y6>0,IF(Y9>=Y6,'Ilmenite',IF(Y9>0,'Both','Sphene')),'None')
if DataCalculating['TiO2'] < 0:
IorS = 'None'
else:
if DataCalculating['FeO'] >= DataCalculating['TiO2']:
IorS = 'Ilmenite'
else:
if DataCalculating['FeO'] > 0:
IorS = 'Both'
else:
IorS = 'Sphene'
# =IF(Z26='Ilmenite',Y6,IF(Z26='Both',Y9,0))
# =IF(Z26='Sphene',Y6,IF(Z26='Both',Y6-Z6,0))
if IorS == 'Ilmenite':
DataCalculating['TiO2'] = DataCalculating['TiO2']
DataCalculating['MnO'] = 0
elif IorS == 'Sphene':
DataCalculating['MnO'] = DataCalculating['TiO2']
DataCalculating['TiO2'] = 0
elif IorS == 'Both':
DataCalculating['MnO'] = DataCalculating['TiO2'] - DataCalculating['FeO']
DataCalculating['TiO2'] = DataCalculating['FeO']
else:
DataCalculating['TiO2'] = 0
DataCalculating['MnO'] = 0
DataCalculating['FeO'] -= DataCalculating['TiO2']
Ilmenite = DataCalculating['TiO2']
# =IF(Z16>0,IF(Z12>=Z16,'Calcite',IF(Z12>0,'Both','Na2CO3')),'None')
if DataCalculating['CO2'] <= 0:
CorN = 'None'
else:
if DataCalculating['CaO'] >= DataCalculating['CO2']:
CorN = 'Calcite'
else:
if DataCalculating['CaO'] > 0:
CorN = 'Both'
else:
CorN = 'Na2CO3'
# =IF(AA26='Calcite',Z16,IF(AA26='Both',Z12,0))
# =IF(AA26='Na2CO3',Z16,IF(AA26='Both',Z16-AA16,0))
if CorN == 'None':
DataCalculating['CO2'] = 0
DataCalculating['SO3'] = 0
elif CorN == 'Calcite':
DataCalculating['CO2'] = DataCalculating['CO2']
DataCalculating['SO3'] = 0
elif CorN == 'Na2CO3':
DataCalculating['SO3'] = DataCalculating['SO3']
DataCalculating['CO2'] = 0
elif CorN == 'Both':
DataCalculating['SO3'] = DataCalculating['CO2'] - DataCalculating['CaO']
DataCalculating['CO2'] = DataCalculating['CaO']
DataCalculating['CaO'] -= DataCalculating['CO2']
Calcite = DataCalculating['CO2']
Na2CO3 = DataCalculating['SO3']
# =IF(AA17>Z13,0,Z13-AA17)
if DataCalculating['SO3'] > DataCalculating['Na2O']:
DataCalculating['Na2O'] = 0
else:
DataCalculating['Na2O'] -= DataCalculating['SO3']
DataCalculating['SiO2'] -= DataCalculating['Zr']
Zircon = DataCalculating['Zr']
# =IF(AB14>0,IF(AB7>=AB14,'Orthoclase',IF(AB7>0,'Both','K2SiO3')),'None')
if DataCalculating['K2O'] <= 0:
OorK = 'None'
else:
if DataCalculating['Al2O3'] >= DataCalculating['K2O']:
OorK = 'Orthoclase'
else:
if DataCalculating['Al2O3'] > 0:
OorK = 'Both'
else:
OorK = 'K2SiO3'
# =IF(AC26='Orthoclase',AB14,IF(AC26='Both',AB7,0))
# =IF(AC26='K2SiO3',AB14,IF(AC26='Both',AB14-AB7,0))
if OorK == 'None':
DataCalculating['K2O'] = 0
DataCalculating['P2O5'] = 0
elif OorK == 'Orthoclase':
DataCalculating['K2O'] = DataCalculating['K2O']
DataCalculating['P2O5'] = 0
elif OorK == 'K2SiO3':
DataCalculating['P2O5'] = DataCalculating['K2O']
DataCalculating['K2O'] = 0
elif OorK == 'Both':
DataCalculating['P2O5'] = DataCalculating['K2O'] - DataCalculating['Al2O3']
DataCalculating['K2O'] = DataCalculating['Al2O3']
DataCalculating['Al2O3'] -= DataCalculating['K2O']
# =IF(AC13>0,IF(AC7>=AC13,'Albite',IF(AC7>0,'Both','Na2SiO3')),'None')
if DataCalculating['Na2O'] <= 0:
AorN = 'None'
else:
if DataCalculating['Al2O3'] >= DataCalculating['Na2O']:
AorN = 'Albite'
else:
if DataCalculating['Al2O3'] > 0:
AorN = 'Both'
else:
AorN = 'Na2SiO3'
# =IF(AND(AC7>=AC13,AC7>0),AC7-AC13,0)
if DataCalculating['Al2O3'] >= DataCalculating['Na2O'] and DataCalculating['Al2O3'] > 0:
DataCalculating['Al2O3'] -= DataCalculating['Na2O']
else:
DataCalculating['Al2O3'] = 0
# =IF(AD26='Albite',AC13,IF(AD26='Both',AC7,0))
# =IF(AD26='Na2SiO3',AC13,IF(AD26='Both',AC13-AD13,0))
if AorN == 'Albite':
DataCalculating['Cl'] = 0
elif AorN == 'Both':
DataCalculating['Cl'] = DataCalculating['Na2O'] - DataCalculating['Al2O3']
DataCalculating['Na2O'] = DataCalculating['Al2O3']
elif AorN == 'Na2SiO3':
DataCalculating['Cl'] = DataCalculating['Na2O']
DataCalculating['Na2O'] = 0
elif AorN == 'None':
DataCalculating['Na2O'] = 0
DataCalculating['Cl'] = 0
# =IF(AD7>0,IF(AD12>0,'Anorthite','None'),'None')
'''
Seem like should be =IF(AD7>0,IF(AD12>AD7,'Anorthite','Corundum'),'None')
If Al2O3 is left after alloting orthoclase and albite, then:
Anorthite = Al2O3, CaO = CaO - Al2O3, SiO2 = SiO2 - 2 Al2O3, Al2O3 = 0
If Al2O3 exceeds CaO in the preceding calculation, then:
Anorthite = CaO, Al2O3 = Al2O3 - CaO, SiO2 = SiO2 - 2 CaO
Corundum = Al2O3, CaO =0, Al2O3 = 0
if DataCalculating['Al2O3']<=0:
AorC='None'
else:
if DataCalculating['CaO']>DataCalculating['Al2O3']:
AorC= 'Anorthite'
else:
Aorc='Corundum'
'''
if DataCalculating['Al2O3'] <= 0:
AorC = 'None'
else:
if DataCalculating['CaO'] > 0:
AorC = 'Anorthite'
else:
Aorc = 'None'
# =IF(AE26='Anorthite',IF(AD12>AD7,0,AD7-AD12),AD7)
# =IF(AE26='Anorthite',IF(AD7>AD12,0,AD12-AD7),AD12)
# =IF(AE26='Anorthite',IF(AD7>AD12,AD12,AD7),0)
if AorC == 'Anorthite':
if DataCalculating['Al2O3'] >= DataCalculating['CaO']:
DataCalculating['Sr'] = DataCalculating['CaO']
DataCalculating['Al2O3'] -= DataCalculating['CaO']
DataCalculating['CaO'] = 0
else:
DataCalculating['Sr'] = DataCalculating['Al2O3']
DataCalculating['CaO'] -= DataCalculating['Al2O3']
DataCalculating['Al2O3'] = 0
else:
DataCalculating['Sr'] = 0
Corundum = DataCalculating['Al2O3']
Anorthite = DataCalculating['Sr']
# =IF(AE10>0,IF(AE12>=AE10,'Sphene',IF(AE12>0,'Both','Rutile')),'None')
if DataCalculating['MnO'] <= 0:
SorR = 'None'
else:
if DataCalculating['CaO'] >= DataCalculating['MnO']:
SorR = 'Sphene'
elif DataCalculating['CaO'] > 0:
SorR = 'Both'
else:
SorR = 'Rutile'
# =IF(AF26='Sphene',AE10,IF(AF26='Both',AE12,0))
# =IF(AF26='Rutile',AE10,IF(AF26='Both',AE10-AE12,0))
if SorR == 'Sphene':
DataCalculating['MnO'] = DataCalculating['MnO']
DataCalculating['S'] = 0
elif SorR == 'Rutile':
DataCalculating['S'] = DataCalculating['MnO']
DataCalculating['MnO'] = 0
elif SorR == 'Both':
DataCalculating['S'] = DataCalculating['MnO'] - DataCalculating['CaO']
DataCalculating['MnO'] = DataCalculating['CaO']
elif SorR == 'None':
DataCalculating['MnO'] = 0
DataCalculating['S'] = 0
DataCalculating['CaO'] -= DataCalculating['MnO']
Rutile = DataCalculating['S']
# =IF(AND(AF20>0),IF(AF8>=AF20,'Acmite',IF(AF8>0,'Both','Na2SiO3')),'None')
if DataCalculating['Cl'] <= 0:
ACorN = 'None'
else:
if DataCalculating['Fe2O3'] >= DataCalculating['Cl']:
ACorN = 'Acmite'
else:
if DataCalculating['Fe2O3'] > 0:
ACorN = 'Both'
else:
ACorN = 'Na2SiO3'
# =IF(AG26='Acmite',AF20,IF(AG26='Both',AF8,0))
# =IF(AG26='Na2SiO3',AF20,IF(AG26='Both',AF20-AG19,0))
if ACorN == 'Acmite':
DataCalculating['F'] = DataCalculating['Cl']
DataCalculating['Cl'] = 0
elif ACorN == 'Na2SiO3':
DataCalculating['Cl'] = DataCalculating['Cl']
DataCalculating['F'] = 0
elif ACorN == 'Both':
DataCalculating['F'] = DataCalculating['Fe2O3']
DataCalculating['Cl'] = DataCalculating['Cl'] - DataCalculating['F']
elif ACorN == 'None':
DataCalculating['F'] = 0
DataCalculating['Cl'] = 0
DataCalculating['Fe2O3'] -= DataCalculating['F']
Acmite = DataCalculating['F']
# =IF(AG8>0,IF(AG9>=AG8,'Magnetite',IF(AG9>0,'Both','Hematite')),'None')
if DataCalculating['Fe2O3'] <= 0:
MorH = 'None'
else:
if DataCalculating['FeO'] >= DataCalculating['Fe2O3']:
MorH = 'Magnetite'
else:
if DataCalculating['FeO'] > 0:
MorH = 'Both'
else:
MorH = 'Hematite'
# =IF(AH26='Magnetite',AG8,IF(AH26='Both',AG9,0))
# =IF(AH26='Hematite',AG8,IF(AH26='Both',AG8-AG9,0))
if MorH == 'Magnetite':
DataCalculating['Fe2O3'] = DataCalculating['Fe2O3']
DataCalculating['Ba'] = 0
elif MorH == 'Hematite':
DataCalculating['Fe2O3'] = 0
DataCalculating['Ba'] = DataCalculating['FeO']
elif MorH == 'Both':
DataCalculating['Fe2O3'] = DataCalculating['FeO']
DataCalculating['Ba'] = DataCalculating['Fe2O3'] - DataCalculating['FeO']
elif MorH == 'None':
DataCalculating['Fe2O3'] = 0
DataCalculating['Ba'] == 0
DataCalculating['FeO'] -= DataCalculating['Fe2O3']
Magnetite = DataCalculating['Fe2O3']
Hematite = DataCalculating['Ba']
# =IF(AH11>0,AH11/(AH11+AH9),0)
Fe2 = DataCalculating['FeO']
Mg = DataCalculating['MgO']
if Mg > 0:
DataCalced.update({'Mg/(Mg+Fe2+) in silicates': 100 * Mg / (Mg + Fe2)})
else:
DataCalced.update({'Mg/(Mg+Fe2+) in silicates': 0})
DataCalculating['FeO'] += DataCalculating['MgO']
DataCalculating['MgO'] = 0
# =IF(AI12>0,IF(AI9>=AI12,'Diopside',IF(AI9>0,'Both','Wollastonite')),'None')
if DataCalculating['CaO'] <= 0:
DorW = 'None'
else:
if DataCalculating['FeO'] >= DataCalculating['CaO']:
DorW = 'Diopside'
else:
if DataCalculating['FeO'] > 0:
DorW = 'Both'
else:
DorW = 'Wollastonite'
# =IF(AJ26='Diopside',AI12,IF(AJ26='Both',AI9,0))
# =IF(AJ26='Wollastonite',AI12,IF(AJ26='Both',AI12-AI9,0))
if DorW == 'Diopside':
DataCalculating['CaO'] = DataCalculating['CaO']
DataCalculating['S'] = 0
elif DorW == 'Wollastonite':
DataCalculating['S'] = DataCalculating['CaO']
DataCalculating['CaO'] = 0
elif DorW == 'Both':
DataCalculating['S'] = DataCalculating['CaO'] - DataCalculating['FeO']
DataCalculating['CaO'] = DataCalculating['FeO']
elif DorW == 'None':
DataCalculating['CaO'] = 0
DataCalculating['S'] = 0
DataCalculating['FeO'] -= DataCalculating['CaO']
Diopside = DataCalculating['CaO']
Quartz = DataCalculating['SiO2']
Zircon = DataCalculating['Zr']
K2SiO3 = DataCalculating['P2O5']
Na2SiO3 = DataCalculating['Cl']
Sphene = DataCalculating['MnO']
Hypersthene = DataCalculating['FeO']
Albite = DataCalculating['Na2O']
Orthoclase = DataCalculating['K2O']
Wollastonite = DataCalculating['S']
# =AJ5-(AL6)-(AL7)-(AL8*2)-(AL12)-(AL9)-(AL10*4)-(AL11*2)-(AL13)-(AL14*6)-(AL15*6)-(AL16)
Quartz -= (Zircon +
K2SiO3 +
Anorthite * 2 +
Na2SiO3 +
Acmite * 4 +
Diopside * 2 +
Sphene +
Hypersthene +
Albite * 6 +
Orthoclase * 6 +
Wollastonite)
# =IF(AL5>0,AL5,0)
if Quartz > 0:
Quartz = Quartz
else:
Quartz = 0
# =IF(AL13>0,IF(AL5>=0,'Hypersthene',IF(AL13+(2*AL5)>0,'Both','Olivine')),'None')
if Hypersthene <= 0:
HorO = 'None'
else:
if Quartz >= 0:
HorO = 'Hypersthene'
else:
if Hypersthene + 2 * Quartz > 0:
HorO = 'Both'
else:
HorO = 'Olivine'
# =IF(AN26='Hypersthene',AL13,IF(AN26='Both',AL13+(2*AL5),0))
# =IF(AN26='Olivine',AL13*0.5,IF(AN26='Both',ABS(AL5),0))
Old_Hypersthene = Hypersthene
if HorO == 'Hypersthene':
Hypersthene = Hypersthene
Olivine = 0
elif HorO == 'Both':
Hypersthene = Hypersthene + Quartz * 2
Olivine = abs(Quartz)
elif HorO == 'Olivine':
Olivine = Hypersthene / 2
Hypersthene = 0
elif HorO == 'None':
Hypersthene = 0
Olivine = 0
# =AL5+AL13-(AN13+AN17)
Quartz += Old_Hypersthene - (Hypersthene + Olivine)
# =IF(AL12>0,IF(AN5>=0,'Sphene',IF(AL12+AN5>0,'Both','Perovskite')),'None')
if Sphene <= 0:
SorP = 'None'
else:
if Quartz >= 0:
SorP = 'Sphene'
else:
if Sphene + Quartz > 0:
SorP = 'Both'
else:
SorP = 'Perovskite'
# =IF(AO26='Sphene',AL12,IF(AO26='Both',AL12+AN5,0))
# =IF(AO26='Perovskite',AL12,IF(AO26='Both',AL12-AO12,0))
Old_Sphene = Sphene
if SorP == 'Sphene':
Sphene = Sphene
Perovskite = 0
elif SorP == 'Perovskite':
Perovskite = Sphene
Sphene = 0
elif SorP == 'Both':
Sphene += Quartz
Perovskite = Old_Sphene - Sphene
elif SorP == 'None':
Sphene = 0
Perovskite = 0
Quartz += Old_Sphene - Sphene
# =IF(AL14>0,IF(AO5>=0,'Albite',IF(AL14+(AO5/4)>0,'Both','Nepheline')),'None')
if Albite <= 0:
AlorNe = 'None'
else:
if Quartz >= 0:
AlorNe = 'Albite'
else:
if Albite + (Quartz / 4) > 0:
AlorNe = 'Both'
else:
AlorNe = 'Nepheline'
# =AO5+(6*AL14)-(AP14*6)-(AP19*2)
# =IF(AP26='Albite',AL14,IF(AP26='Both',AL14+(AO5/4),0))
# =IF(AP26='Nepheline',AL14,IF(AP26='Both',AL14-AP14,0))
Old_Albite = Albite
if AlorNe == 'Albite':
Albite = Albite
Nepheline = 0
elif AlorNe == 'Nepheline':
Nepheline = Albite
Albite = 0
elif AlorNe == 'Both':
Albite += Quartz / 4
Nepheline = Old_Albite - Albite
elif AlorNe == 'None':
Nepheline = 0
Albite = 0
Quartz += (6 * Old_Albite) - (Albite * 6) - (Nepheline * 2)
# =IF(AL8=0,0,AL8/(AL8+(AP14*2)))
if Anorthite == 0:
DataCalced.update({'Plagioclase An content': 0})
else:
DataCalced.update({'Plagioclase An content': 100 * Anorthite / (Anorthite + 2 * Albite)})
# =IF(AL15>0,IF(AP5>=0,'Orthoclase',IF(AL15+(AP5/2)>0,'Both','Leucite')),'None')
if Orthoclase <= 0:
OorL = 'None'
else:
if Quartz >= 0:
OorL = 'Orthoclase'
else:
if Orthoclase + Quartz / 2 > 0:
OorL = 'Both'
else:
OorL = 'Leucite'
# =IF(AQ26='Orthoclase',AL15,IF(AQ26='Both',AL15+(AP5/2),0))
# =IF(AQ26='Leucite',AL15,IF(AQ26='Both',AL15-AQ15,0))
Old_Orthoclase = Orthoclase
if OorL == 'Orthoclase':
Orthoclase = Orthoclase
Leucite = 0
elif OorL == 'Leucite':
Leucite = Orthoclase
Orthoclase = 0
elif OorL == 'Both':
Orthoclase += Quartz / 2
Leucite = Old_Orthoclase - Orthoclase
elif OorL == 'None':
Orthoclase = 0
Leucite = 0
# =AP5+(AL15*6)-(AQ15*6)-(AQ20*4)
Quartz += (Old_Orthoclase * 6) - (Orthoclase * 6) - (Leucite * 4)
# =IF(AL16>0,IF(AQ5>=0,'Wollastonite',IF(AL16+(AQ5*2)>0,'Both','Larnite')),'None')
if Wollastonite <= 0:
WorB = 'None'
else:
if Quartz >= 0:
WorB = 'Wollastonite'
else:
if Wollastonite + Quartz / 2 > 0:
WorB = 'Both'
else:
WorB = 'Larnite'
# =IF(AR26='Wollastonite',AL16,IF(AR26='Both',AL16+(2*AQ5),0))
# =IF(AR26='Larnite',AL16/2,IF(AR26='Both',(AL16-AR16)/2,0))
Old_Wollastonite = Wollastonite
if WorB == 'Wollastonite':
Wollastonite = Wollastonite
Larnite = 0
elif WorB == 'Larnite':
Larnite = Wollastonite / 2
Wollastonite = 0
elif WorB == 'Both':
Wollastonite += Quartz * 2
Larnite = (Old_Wollastonite - Wollastonite) / 2
elif WorB == 'None':
Wollastonite = 0
Larnite = 0
# =AQ5+AL16-AR16-AR21
Quartz += Old_Wollastonite - Wollastonite - Larnite
# =IF(AL11>0,IF(AR5>=0,'Diopside',IF(AL11+AR5>0,'Both','LarniteOlivine')),'None')
if Diopside <= 0:
DorL = 'None'
else:
if Quartz >= 0:
DorL = 'Diopside'
else:
if Diopside + Quartz > 0:
DorL = 'Both'
else:
DorL = 'LarniteOlivine'
# =IF(AS26='Diopside',AL11,IF(AS26='Both',AL11+AR5,0))
# =(IF(AS26='LarniteOlivine',AL11/2,IF(AS26='Both',(AL11-AS11)/2,0)))+AN17
# =(IF(AS26='LarniteOlivine',AL11/2,IF(AS26='Both',(AL11-AS11)/2,0)))+AR21
Old_Diopside = Diopside
Old_Larnite = Larnite
Old_Olivine = Olivine
if DorL == 'Diopside':
Diopside = Diopside
elif DorL == 'LarniteOlivine':
Larnite += Diopside / 2
Olivine += Diopside / 2
Diopside = 0
elif DorL == 'Both':
Diopside += Quartz
Larnite += Old_Diopside - Diopside
Olivine += Old_Diopside - Diopside
elif DorL == 'None':
Diopside = 0
# =AR5+(AL11*2)+AN17+AR21-AS21-(AS11*2)-AS17
Quartz += (Old_Diopside * 2) + Old_Olivine + Old_Larnite - Larnite - (Diopside * 2) - Olivine
# =IF(AQ20>0,IF(AS5>=0,'Leucite',IF(AQ20+(AS5/2)>0,'Both','Kalsilite')),'None')
if Leucite <= 0:
LorK = 'None'
else:
if Quartz >= 0:
LorK = 'Leucite'
else:
if Leucite + Quartz / 2 > 0:
LorK = 'Both'
else:
LorK = 'Kalsilite'
# =IF(AT26='Leucite',AQ20,IF(AT26='Both',AQ20+(AS5/2),0))
# =IF(AT26='Kalsilite',AQ20,IF(AT26='Both',AQ20-AT20,0))
Old_Leucite = Leucite
if LorK == 'Leucite':
Leucite = Leucite
Kalsilite = 0
elif LorK == 'Kalsilite':
Kalsilite = Leucite
Leucite = 0
elif LorK == 'Both':
Leucite += Quartz / 2
Kalsilite = Old_Leucite - Leucite
elif LorK == 'None':
Leucite = 0
Kalsilite = 0
# =AS5+(AQ20*4)-(AT20*4)-(AT22*2)
Quartz += Old_Leucite * 4 - Leucite * 4 - Kalsilite * 2
Q = Quartz
A = Orthoclase
P = Anorthite + Albite
F = Nepheline + Leucite + Kalsilite
DataResult.update({'Quartz': Quartz})
DataResult.update({'Zircon': Zircon})
DataResult.update({'K2SiO3': K2SiO3})
DataResult.update({'Anorthite': Anorthite})
DataResult.update({'Na2SiO3': Na2SiO3})
DataResult.update({'Acmite': Acmite})
DataResult.update({'Diopside': Diopside})
DataResult.update({'Sphene': Sphene})
DataResult.update({'Hypersthene': Hypersthene})
DataResult.update({'Albite': Albite})
DataResult.update({'Orthoclase': Orthoclase})
DataResult.update({'Wollastonite': Wollastonite})
DataResult.update({'Olivine': Olivine})
DataResult.update({'Perovskite': Perovskite})
DataResult.update({'Nepheline': Nepheline})
DataResult.update({'Leucite': Leucite})
DataResult.update({'Larnite': Larnite})
DataResult.update({'Kalsilite': Kalsilite})
DataResult.update({'Apatite': Apatite})
DataResult.update({'Halite': Halite})
DataResult.update({'Fluorite': Fluorite})
DataResult.update({'Anhydrite': Anhydrite})
DataResult.update({'Thenardite': Thenardite})
DataResult.update({'Pyrite': Pyrite})
DataResult.update({'Magnesiochromite': Magnesiochromite})
DataResult.update({'Chromite': Chromite})
DataResult.update({'Ilmenite': Ilmenite})
DataResult.update({'Calcite': Calcite})
DataResult.update({'Na2CO3': Na2CO3})
DataResult.update({'Corundum': Corundum})
DataResult.update({'Rutile': Rutile})
DataResult.update({'Magnetite': Magnetite})
DataResult.update({'Hematite': Hematite})
DataResult.update({'Q Mole': Q})
DataResult.update({'A Mole': A})
DataResult.update({'P Mole': P})
DataResult.update({'F Mole': F})
DataWeight.update({'Quartz': Quartz * self.DataBase['Quartz'][0]})
DataWeight.update({'Zircon': Zircon * self.DataBase['Zircon'][0]})
DataWeight.update({'K2SiO3': K2SiO3 * self.DataBase['K2SiO3'][0]})
DataWeight.update({'Anorthite': Anorthite * self.DataBase['Anorthite'][0]})
DataWeight.update({'Na2SiO3': Na2SiO3 * self.DataBase['Na2SiO3'][0]})
DataWeight.update({'Acmite': Acmite * self.DataBase['Acmite'][0]})
DataWeight.update({'Diopside': Diopside * self.DataBase['Diopside'][0]})
DataWeight.update({'Sphene': Sphene * self.DataBase['Sphene'][0]})
DataWeight.update({'Hypersthene': Hypersthene * self.DataBase['Hypersthene'][0]})
DataWeight.update({'Albite': Albite * self.DataBase['Albite'][0]})
DataWeight.update({'Orthoclase': Orthoclase * self.DataBase['Orthoclase'][0]})
DataWeight.update({'Wollastonite': Wollastonite * self.DataBase['Wollastonite'][0]})
DataWeight.update({'Olivine': Olivine * self.DataBase['Olivine'][0]})
DataWeight.update({'Perovskite': Perovskite * self.DataBase['Perovskite'][0]})
DataWeight.update({'Nepheline': Nepheline * self.DataBase['Nepheline'][0]})
DataWeight.update({'Leucite': Leucite * self.DataBase['Leucite'][0]})
DataWeight.update({'Larnite': Larnite * self.DataBase['Larnite'][0]})
DataWeight.update({'Kalsilite': Kalsilite * self.DataBase['Kalsilite'][0]})
DataWeight.update({'Apatite': Apatite * self.DataBase['Apatite'][0]})
DataWeight.update({'Halite': Halite * self.DataBase['Halite'][0]})
DataWeight.update({'Fluorite': Fluorite * self.DataBase['Fluorite'][0]})
DataWeight.update({'Anhydrite': Anhydrite * self.DataBase['Anhydrite'][0]})
DataWeight.update({'Thenardite': Thenardite * self.DataBase['Thenardite'][0]})
DataWeight.update({'Pyrite': Pyrite * self.DataBase['Pyrite'][0]})
DataWeight.update({'Magnesiochromite': Magnesiochromite * self.DataBase['Magnesiochromite'][0]})
DataWeight.update({'Chromite': Chromite * self.DataBase['Chromite'][0]})
DataWeight.update({'Ilmenite': Ilmenite * self.DataBase['Ilmenite'][0]})
DataWeight.update({'Calcite': Calcite * self.DataBase['Calcite'][0]})
DataWeight.update({'Na2CO3': Na2CO3 * self.DataBase['Na2CO3'][0]})
DataWeight.update({'Corundum': Corundum * self.DataBase['Corundum'][0]})
DataWeight.update({'Rutile': Rutile * self.DataBase['Rutile'][0]})
DataWeight.update({'Magnetite': Magnetite * self.DataBase['Magnetite'][0]})
DataWeight.update({'Hematite': Hematite * self.DataBase['Hematite'][0]})
DataWeight.update({'Q Weight': Quartz * self.DataBase['Quartz'][0]})
DataWeight.update({'A Weight': Orthoclase * self.DataBase['Orthoclase'][0]})
DataWeight.update({'P Weight': Anorthite * self.DataBase['Anorthite'][0] + Albite * self.DataBase['Albite'][0]})
DataWeight.update({'F Weight': Nepheline * self.DataBase['Nepheline'][0] + Leucite * self.DataBase['Leucite'][0] + Kalsilite * self.DataBase['Kalsilite'][0]})
WholeVolume = 0
WholeMole = 0
tmpVolume = []
tmpVolume.append(Quartz * self.DataBase['Quartz'][0] / self.DataBase['Quartz'][1])
tmpVolume.append(Zircon * self.DataBase['Zircon'][0] / self.DataBase['Zircon'][1])
tmpVolume.append(K2SiO3 * self.DataBase['K2SiO3'][0] / self.DataBase['K2SiO3'][1])
tmpVolume.append(Anorthite * self.DataBase['Anorthite'][0] / self.DataBase['Anorthite'][1])
tmpVolume.append(Na2SiO3 * self.DataBase['Na2SiO3'][0] / self.DataBase['Na2SiO3'][1])
tmpVolume.append(Acmite * self.DataBase['Acmite'][0] / self.DataBase['Acmite'][1])
tmpVolume.append(Diopside * self.DataBase['Diopside'][0] / self.DataBase['Diopside'][1])
tmpVolume.append(Sphene * self.DataBase['Sphene'][0] / self.DataBase['Sphene'][1])
tmpVolume.append(Hypersthene * self.DataBase['Hypersthene'][0] / self.DataBase['Hypersthene'][1])
tmpVolume.append(Albite * self.DataBase['Albite'][0] / self.DataBase['Albite'][1])
tmpVolume.append(Orthoclase * self.DataBase['Orthoclase'][0] / self.DataBase['Orthoclase'][1])
tmpVolume.append(Wollastonite * self.DataBase['Wollastonite'][0] / self.DataBase['Wollastonite'][1])
tmpVolume.append(Olivine * self.DataBase['Olivine'][0] / self.DataBase['Olivine'][1])
tmpVolume.append(Perovskite * self.DataBase['Perovskite'][0] / self.DataBase['Perovskite'][1])
tmpVolume.append(Nepheline * self.DataBase['Nepheline'][0] / self.DataBase['Nepheline'][1])
tmpVolume.append(Leucite * self.DataBase['Leucite'][0] / self.DataBase['Leucite'][1])
tmpVolume.append(Larnite * self.DataBase['Larnite'][0] / self.DataBase['Larnite'][1])
tmpVolume.append(Kalsilite * self.DataBase['Kalsilite'][0] / self.DataBase['Kalsilite'][1])
tmpVolume.append(Apatite * self.DataBase['Apatite'][0] / self.DataBase['Apatite'][1])
tmpVolume.append(Halite * self.DataBase['Halite'][0] / self.DataBase['Halite'][1])
tmpVolume.append(Fluorite * self.DataBase['Fluorite'][0] / self.DataBase['Fluorite'][1])
tmpVolume.append(Anhydrite * self.DataBase['Anhydrite'][0] / self.DataBase['Anhydrite'][1])
tmpVolume.append(Thenardite * self.DataBase['Thenardite'][0] / self.DataBase['Thenardite'][1])
tmpVolume.append(Pyrite * self.DataBase['Pyrite'][0] / self.DataBase['Pyrite'][1])
tmpVolume.append(Magnesiochromite * self.DataBase['Magnesiochromite'][0] / self.DataBase['Magnesiochromite'][1])
tmpVolume.append(Chromite * self.DataBase['Chromite'][0] / self.DataBase['Chromite'][1])
tmpVolume.append(Ilmenite * self.DataBase['Ilmenite'][0] / self.DataBase['Ilmenite'][1])
tmpVolume.append(Calcite * self.DataBase['Calcite'][0] / self.DataBase['Calcite'][1])
tmpVolume.append(Na2CO3 * self.DataBase['Na2CO3'][0] / self.DataBase['Na2CO3'][1])
tmpVolume.append(Corundum * self.DataBase['Corundum'][0] / self.DataBase['Corundum'][1])
tmpVolume.append(Rutile * self.DataBase['Rutile'][0] / self.DataBase['Rutile'][1])
tmpVolume.append(Magnetite * self.DataBase['Magnetite'][0] / self.DataBase['Magnetite'][1])
tmpVolume.append(Hematite * self.DataBase['Hematite'][0] / self.DataBase['Hematite'][1])
WholeVolume = sum(tmpVolume)
DataVolume.update(
{'Quartz': (Quartz * self.DataBase['Quartz'][0] / self.DataBase['Quartz'][1]) / WholeVolume * 100})
DataVolume.update(
{'Zircon': (Zircon * self.DataBase['Zircon'][0] / self.DataBase['Zircon'][1]) / WholeVolume * 100})
DataVolume.update(
{'K2SiO3': (K2SiO3 * self.DataBase['K2SiO3'][0] / self.DataBase['K2SiO3'][1]) / WholeVolume * 100})
DataVolume.update({'Anorthite': (Anorthite * self.DataBase['Anorthite'][0] / self.DataBase['Anorthite'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Na2SiO3': (Na2SiO3 * self.DataBase['Na2SiO3'][0] / self.DataBase['Na2SiO3'][1]) / WholeVolume * 100})
DataVolume.update(
{'Acmite': (Acmite * self.DataBase['Acmite'][0] / self.DataBase['Acmite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Diopside': (Diopside * self.DataBase['Diopside'][0] / self.DataBase['Diopside'][1]) / WholeVolume * 100})
DataVolume.update(
{'Sphene': (Sphene * self.DataBase['Sphene'][0] / self.DataBase['Sphene'][1]) / WholeVolume * 100})
DataVolume.update({'Hypersthene': (Hypersthene * self.DataBase['Hypersthene'][0] / self.DataBase['Hypersthene'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Albite': (Albite * self.DataBase['Albite'][0] / self.DataBase['Albite'][1]) / WholeVolume * 100})
DataVolume.update({'Orthoclase': (Orthoclase * self.DataBase['Orthoclase'][0] / self.DataBase['Orthoclase'][
1]) / WholeVolume * 100})
DataVolume.update({'Wollastonite': (Wollastonite * self.DataBase['Wollastonite'][0] /
self.DataBase['Wollastonite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Olivine': (Olivine * self.DataBase['Olivine'][0] / self.DataBase['Olivine'][1]) / WholeVolume * 100})
DataVolume.update({'Perovskite': (Perovskite * self.DataBase['Perovskite'][0] / self.DataBase['Perovskite'][
1]) / WholeVolume * 100})
DataVolume.update({'Nepheline': (Nepheline * self.DataBase['Nepheline'][0] / self.DataBase['Nepheline'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Leucite': (Leucite * self.DataBase['Leucite'][0] / self.DataBase['Leucite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Larnite': (Larnite * self.DataBase['Larnite'][0] / self.DataBase['Larnite'][1]) / WholeVolume * 100})
DataVolume.update({'Kalsilite': (Kalsilite * self.DataBase['Kalsilite'][0] / self.DataBase['Kalsilite'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Apatite': (Apatite * self.DataBase['Apatite'][0] / self.DataBase['Apatite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Halite': (Halite * self.DataBase['Halite'][0] / self.DataBase['Halite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Fluorite': (Fluorite * self.DataBase['Fluorite'][0] / self.DataBase['Fluorite'][1]) / WholeVolume * 100})
DataVolume.update({'Anhydrite': (Anhydrite * self.DataBase['Anhydrite'][0] / self.DataBase['Anhydrite'][
1]) / WholeVolume * 100})
DataVolume.update({'Thenardite': (Thenardite * self.DataBase['Thenardite'][0] / self.DataBase['Thenardite'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Pyrite': (Pyrite * self.DataBase['Pyrite'][0] / self.DataBase['Pyrite'][1]) / WholeVolume * 100})
DataVolume.update({'Magnesiochromite': (Magnesiochromite * self.DataBase['Magnesiochromite'][0] /
self.DataBase['Magnesiochromite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Chromite': (Chromite * self.DataBase['Chromite'][0] / self.DataBase['Chromite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Ilmenite': (Ilmenite * self.DataBase['Ilmenite'][0] / self.DataBase['Ilmenite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Calcite': (Calcite * self.DataBase['Calcite'][0] / self.DataBase['Calcite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Na2CO3': (Na2CO3 * self.DataBase['Na2CO3'][0] / self.DataBase['Na2CO3'][1]) / WholeVolume * 100})
DataVolume.update(
{'Corundum': (Corundum * self.DataBase['Corundum'][0] / self.DataBase['Corundum'][1]) / WholeVolume * 100})
DataVolume.update(
{'Rutile': (Rutile * self.DataBase['Rutile'][0] / self.DataBase['Rutile'][1]) / WholeVolume * 100})
DataVolume.update({'Magnetite': (Magnetite * self.DataBase['Magnetite'][0] / self.DataBase['Magnetite'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Hematite': (Hematite * self.DataBase['Hematite'][0] / self.DataBase['Hematite'][1]) / WholeVolume * 100})
DataVolume.update({'Q': DataVolume['Quartz']})
DataVolume.update({'A': DataVolume['Orthoclase']})
DataVolume.update({'P': DataVolume['Anorthite'] + DataVolume['Albite']})
DataVolume.update({'F': DataVolume['Nepheline'] + DataVolume['Leucite'] + DataVolume['Kalsilite']})
DI = 0
# for i in ['Quartz', 'Anorthite', 'Albite', 'Orthoclase', 'Nepheline', 'Leucite', 'Kalsilite']:
# exec('DI+=' + i + '*self.DataBase[\'' + i + '\'][0]')
DI = Quartz + Anorthite + Albite + Orthoclase + Nepheline + Leucite + Kalsilite
DiWeight=0
DiVolume=0
DiWeight = DataWeight['Quartz']+DataWeight['Anorthite']+DataWeight['Albite']+DataWeight['Orthoclase']+DataWeight['Nepheline']+DataWeight['Leucite']+DataWeight['Kalsilite']
DiVolume = DataVolume['Quartz']+DataVolume['Anorthite']+DataVolume['Albite']+DataVolume['Orthoclase']+DataVolume['Nepheline']+DataVolume['Leucite']+DataVolume['Kalsilite']
# print('\n\n DI is\n',DI,'\n\n')
DataCalced.update({'Differentiation Index Weight': DiWeight})
DataCalced.update({'Differentiation Index Volume': DiVolume})
return (DataResult, DataWeight, DataVolume, DataCalced) | python | def singleCalc(self,
m={'Al2O3': 13.01, 'Alpha': 0.6, 'Ba': 188.0, 'Be': 0.85, 'CaO': 8.35, 'Ce': 28.2, 'Co': 45.2,
'Cr': 117.0, 'Cs': 0.83, 'Cu': 53.5, 'Dy': 5.58, 'Er': 2.96, 'Eu': 1.79, 'Fe2O3': 14.47,
'FeO': 5.51, 'Ga': 19.4, 'Gd': 5.24, 'Hf': 3.38, 'Ho': 1.1, 'K2O': 0.72, 'LOI': 5.05,
'La': 11.4, 'Label': 'ZhangSH2016', 'Li': 15.0, 'Lu': 0.39, 'Mg
'MnO': 0.21, 'Na2O': 1.88, 'Nb': 12.6, 'Nd': 18.4, 'Ni': 69.4, 'P2O5': 0.23, 'Pb': 3.17,
'Pr': 3.95, 'Rb': 18.4, 'Sc': 37.4, 'SiO2': 48.17, 'Size': 10, 'Sm': 5.08, 'Sr': 357,
'Ta': 0.77, 'Tb': 0.88, 'Th': 1.85, 'TiO2': 2.56, 'Tl': 0.06, 'Tm': 0.44, 'Total': 99.91,
'U': 0.41, 'V': 368.0, 'Y': 29.7, 'Yb': 2.68, 'Zn': 100.0, 'Zr': 130.0, }):
DataResult={}
DataWeight={}
DataVolume={}
DataCalced={}
DataResult.update({'Label': m['Label']})
DataWeight.update({'Label': m['Label']})
DataVolume.update({'Label': m['Label']})
DataCalced.update({'Label': m['Label']})
DataResult.update({'Width': m['Width']})
DataWeight.update({'Width': m['Width']})
DataVolume.update({'Width': m['Width']})
DataCalced.update({'Width': m['Width']})
DataResult.update({'Style': m['Style']})
DataWeight.update({'Style': m['Style']})
DataVolume.update({'Style': m['Style']})
DataCalced.update({'Style': m['Style']})
DataResult.update({'Alpha': m['Alpha']})
DataWeight.update({'Alpha': m['Alpha']})
DataVolume.update({'Alpha': m['Alpha']})
DataCalced.update({'Alpha': m['Alpha']})
DataResult.update({'Size': m['Size']})
DataWeight.update({'Size': m['Size']})
DataVolume.update({'Size': m['Size']})
DataCalced.update({'Size': m['Size']})
DataResult.update({'Color': m['Color']})
DataWeight.update({'Color': m['Color']})
DataVolume.update({'Color': m['Color']})
DataCalced.update({'Color': m['Color']})
DataResult.update({'Marker': m['Marker']})
DataWeight.update({'Marker': m['Marker']})
DataVolume.update({'Marker': m['Marker']})
DataCalced.update({'Marker': m['Marker']})
WholeMass = 0
EachMole = {}
for j in self.Elements:
try:
T_TMP = m[j]
except(KeyError):
T_TMP = 0
if j == 'Sr':
TMP = T_TMP / (87.62 / 103.619 * 10000)
elif j == 'Ba':
TMP = T_TMP / (137.327 / 153.326 * 10000)
elif j == 'Ni':
TMP = T_TMP / (58.6934 / 74.69239999999999 * 10000)
elif j == 'Cr':
TMP = T_TMP / ((2 * 51.9961) / 151.98919999999998 * 10000)
elif j == 'Zr':
TMP = T_TMP / ((2 * 91.224) / 123.22200000000001 * 10000)
else:
TMP = T_TMP
V = TMP
try:
WholeMass += float(V)
except ValueError:
pass
WeightCorrectionFactor = (100 / WholeMass)
for j in self.Elements:
try:
T_TMP = m[j]
except(KeyError):
T_TMP = 0
if j == 'Sr':
TMP = T_TMP / (87.62 / 103.619 * 10000)
elif j == 'Ba':
TMP = T_TMP / (137.327 / 153.326 * 10000)
elif j == 'Ni':
TMP = T_TMP / (58.6934 / 74.69239999999999 * 10000)
elif j == 'Cr':
TMP = T_TMP / ((2 * 51.9961) / 151.98919999999998 * 10000)
elif j == 'Zr':
TMP = T_TMP / ((91.224) / 123.22200000000001 * 10000)
else:
TMP = T_TMP
try:
M = TMP / self.BaseMass[j] * WeightCorrectionFactor
except TypeError:
pass
EachMole.update({j: M})
DataCalculating = EachMole
Fe3 = DataCalculating['Fe2O3']
Fe2 = DataCalculating['FeO']
Mg = DataCalculating['MgO']
Ca = DataCalculating['CaO']
Na = DataCalculating['Na2O']
try:
DataCalced.update({'Fe3+/(Total Fe) in rock (Mole)': 100 * Fe3 * 2 / (Fe3 * 2 + Fe2)})
except(ZeroDivisionError):
DataCalced.update({'Fe3+/(Total Fe) in rock (Mole)': 0})
pass
try:
DataCalced.update({'Mg/(Mg+Total Fe) in rock (Mole)': 100 * Mg / (Mg + Fe3 * 2 + Fe2)})
except(ZeroDivisionError):
DataCalced.update({'Mg/(Mg+Total Fe) in rock (Mole)': 0})
pass
try:
DataCalced.update({'Mg/(Mg+Fe2+) in rock (Mole)': 100 * Mg / (Mg + Fe2)})
except(ZeroDivisionError):
DataCalced.update({'Mg/(Mg+Fe2+) in rock (Mole)': 0})
pass
try:
DataCalced.update({'Ca/(Ca+Na) in rock (Mole)': 100 * Ca / (Ca + Na * 2)})
except(ZeroDivisionError):
DataCalced.update({'Ca/(Ca+Na) in rock (Mole)': 0})
pass
DataCalculating['CaO'] += DataCalculating['Sr']
DataCalculating['Sr'] = 0
DataCalculating['K2O'] += 2 * DataCalculating['Ba']
DataCalculating['Ba'] = 0
try:
if DataCalculating['CaO'] >= 10 / 3 * DataCalculating['P2O5']:
DataCalculating['CaO'] -= 10 / 3 * DataCalculating['P2O5']
else:
DataCalculating['CaO'] = 0
except(ZeroDivisionError):
pass
DataCalculating['P2O5'] = DataCalculating['P2O5'] / 1.5
Apatite = DataCalculating['P2O5']
if DataCalculating['F'] >= DataCalculating['P2O5']:
DataCalculating['F'] -= DataCalculating['P2O5']
else:
DataCalculating['F'] = 0
if DataCalculating['F'] >= DataCalculating['P2O5']:
DataCalculating['F'] -= DataCalculating['P2O5']
else:
DataCalculating['F'] = 0
if DataCalculating['Na2O'] >= DataCalculating['Cl']:
DataCalculating['Na2O'] -= DataCalculating['Cl']
else:
DataCalculating['Na2O'] = 0
Halite = DataCalculating['Cl']
if DataCalculating['CaO'] >= 0.5 * DataCalculating['F']:
DataCalculating['CaO'] -= 0.5 * DataCalculating['F']
else:
DataCalculating['CaO'] = 0
DataCalculating['F'] *= 0.5
Fluorite = DataCalculating['F']
AorT = 0
if DataCalculating['SO3'] <= 0:
AorT = 'None'
else:
if DataCalculating['Na2O'] >= DataCalculating['SO3']:
AorT = 'Thenardite'
else:
if DataCalculating['Na2O'] > 0:
AorT = 'Both'
else:
AorT = 'Anhydrite'
if AorT == 'Anhydrite':
DataCalculating['Sr'] = 0
elif AorT == 'Thenardite':
DataCalculating['Sr'] = DataCalculating['SO3']
DataCalculating['SO3'] = 0
elif AorT == 'Both':
DataCalculating['Sr'] = DataCalculating['SO3'] - DataCalculating['CaO']
DataCalculating['SO3'] = DataCalculating['CaO']
else:
DataCalculating['SO3'] = 0
DataCalculating['Sr'] = 0
DataCalculating['CaO'] -= DataCalculating['SO3']
DataCalculating['Na2O'] -= DataCalculating['Sr']
Anhydrite = DataCalculating['SO3']
Thenardite = DataCalculating['Sr']
Pyrite = 0.5 * DataCalculating['S']
if DataCalculating['FeO'] >= DataCalculating['S'] * 0.5:
DataCalculating['FeO'] -= DataCalculating['S'] * 0.5
else:
DataCalculating['FeO'] = 0
if DataCalculating['Cr'] > 0:
if DataCalculating['FeO'] >= DataCalculating['Cr']:
CorM = 'Chromite'
elif DataCalculating['FeO'] > 0:
CorM = 'Both'
else:
CorM = 'Magnesiochromite'
else:
CorM = 'None'
if CorM == 'Chromite':
DataCalculating['Cr'] = DataCalculating['Cr']
DataCalculating['Ni'] = 0
elif CorM == 'Magnesiochromite':
DataCalculating['Ni'] = DataCalculating['Cr']
DataCalculating['Cr'] = 0
elif CorM == 'Both':
DataCalculating['Ni'] = DataCalculating['Cr'] - DataCalculating['FeO']
DataCalculating['Cr'] = DataCalculating['FeO']
else:
DataCalculating['Cr'] = 0
DataCalculating['Ni'] = 0
DataCalculating['MgO'] -= DataCalculating['Ni']
Magnesiochromite = DataCalculating['Ni']
Chromite = DataCalculating['Cr']
if DataCalculating['FeO'] >= DataCalculating['Cr']:
DataCalculating['FeO'] -= DataCalculating['Cr']
else:
DataCalculating['FeO'] = 0
if DataCalculating['TiO2'] < 0:
IorS = 'None'
else:
if DataCalculating['FeO'] >= DataCalculating['TiO2']:
IorS = 'Ilmenite'
else:
if DataCalculating['FeO'] > 0:
IorS = 'Both'
else:
IorS = 'Sphene'
if IorS == 'Ilmenite':
DataCalculating['TiO2'] = DataCalculating['TiO2']
DataCalculating['MnO'] = 0
elif IorS == 'Sphene':
DataCalculating['MnO'] = DataCalculating['TiO2']
DataCalculating['TiO2'] = 0
elif IorS == 'Both':
DataCalculating['MnO'] = DataCalculating['TiO2'] - DataCalculating['FeO']
DataCalculating['TiO2'] = DataCalculating['FeO']
else:
DataCalculating['TiO2'] = 0
DataCalculating['MnO'] = 0
DataCalculating['FeO'] -= DataCalculating['TiO2']
Ilmenite = DataCalculating['TiO2']
if DataCalculating['CO2'] <= 0:
CorN = 'None'
else:
if DataCalculating['CaO'] >= DataCalculating['CO2']:
CorN = 'Calcite'
else:
if DataCalculating['CaO'] > 0:
CorN = 'Both'
else:
CorN = 'Na2CO3'
if CorN == 'None':
DataCalculating['CO2'] = 0
DataCalculating['SO3'] = 0
elif CorN == 'Calcite':
DataCalculating['CO2'] = DataCalculating['CO2']
DataCalculating['SO3'] = 0
elif CorN == 'Na2CO3':
DataCalculating['SO3'] = DataCalculating['SO3']
DataCalculating['CO2'] = 0
elif CorN == 'Both':
DataCalculating['SO3'] = DataCalculating['CO2'] - DataCalculating['CaO']
DataCalculating['CO2'] = DataCalculating['CaO']
DataCalculating['CaO'] -= DataCalculating['CO2']
Calcite = DataCalculating['CO2']
Na2CO3 = DataCalculating['SO3']
if DataCalculating['SO3'] > DataCalculating['Na2O']:
DataCalculating['Na2O'] = 0
else:
DataCalculating['Na2O'] -= DataCalculating['SO3']
DataCalculating['SiO2'] -= DataCalculating['Zr']
Zircon = DataCalculating['Zr']
if DataCalculating['K2O'] <= 0:
OorK = 'None'
else:
if DataCalculating['Al2O3'] >= DataCalculating['K2O']:
OorK = 'Orthoclase'
else:
if DataCalculating['Al2O3'] > 0:
OorK = 'Both'
else:
OorK = 'K2SiO3'
if OorK == 'None':
DataCalculating['K2O'] = 0
DataCalculating['P2O5'] = 0
elif OorK == 'Orthoclase':
DataCalculating['K2O'] = DataCalculating['K2O']
DataCalculating['P2O5'] = 0
elif OorK == 'K2SiO3':
DataCalculating['P2O5'] = DataCalculating['K2O']
DataCalculating['K2O'] = 0
elif OorK == 'Both':
DataCalculating['P2O5'] = DataCalculating['K2O'] - DataCalculating['Al2O3']
DataCalculating['K2O'] = DataCalculating['Al2O3']
DataCalculating['Al2O3'] -= DataCalculating['K2O']
if DataCalculating['Na2O'] <= 0:
AorN = 'None'
else:
if DataCalculating['Al2O3'] >= DataCalculating['Na2O']:
AorN = 'Albite'
else:
if DataCalculating['Al2O3'] > 0:
AorN = 'Both'
else:
AorN = 'Na2SiO3'
if DataCalculating['Al2O3'] >= DataCalculating['Na2O'] and DataCalculating['Al2O3'] > 0:
DataCalculating['Al2O3'] -= DataCalculating['Na2O']
else:
DataCalculating['Al2O3'] = 0
if AorN == 'Albite':
DataCalculating['Cl'] = 0
elif AorN == 'Both':
DataCalculating['Cl'] = DataCalculating['Na2O'] - DataCalculating['Al2O3']
DataCalculating['Na2O'] = DataCalculating['Al2O3']
elif AorN == 'Na2SiO3':
DataCalculating['Cl'] = DataCalculating['Na2O']
DataCalculating['Na2O'] = 0
elif AorN == 'None':
DataCalculating['Na2O'] = 0
DataCalculating['Cl'] = 0
if DataCalculating['Al2O3'] <= 0:
AorC = 'None'
else:
if DataCalculating['CaO'] > 0:
AorC = 'Anorthite'
else:
Aorc = 'None'
if AorC == 'Anorthite':
if DataCalculating['Al2O3'] >= DataCalculating['CaO']:
DataCalculating['Sr'] = DataCalculating['CaO']
DataCalculating['Al2O3'] -= DataCalculating['CaO']
DataCalculating['CaO'] = 0
else:
DataCalculating['Sr'] = DataCalculating['Al2O3']
DataCalculating['CaO'] -= DataCalculating['Al2O3']
DataCalculating['Al2O3'] = 0
else:
DataCalculating['Sr'] = 0
Corundum = DataCalculating['Al2O3']
Anorthite = DataCalculating['Sr']
if DataCalculating['MnO'] <= 0:
SorR = 'None'
else:
if DataCalculating['CaO'] >= DataCalculating['MnO']:
SorR = 'Sphene'
elif DataCalculating['CaO'] > 0:
SorR = 'Both'
else:
SorR = 'Rutile'
if SorR == 'Sphene':
DataCalculating['MnO'] = DataCalculating['MnO']
DataCalculating['S'] = 0
elif SorR == 'Rutile':
DataCalculating['S'] = DataCalculating['MnO']
DataCalculating['MnO'] = 0
elif SorR == 'Both':
DataCalculating['S'] = DataCalculating['MnO'] - DataCalculating['CaO']
DataCalculating['MnO'] = DataCalculating['CaO']
elif SorR == 'None':
DataCalculating['MnO'] = 0
DataCalculating['S'] = 0
DataCalculating['CaO'] -= DataCalculating['MnO']
Rutile = DataCalculating['S']
if DataCalculating['Cl'] <= 0:
ACorN = 'None'
else:
if DataCalculating['Fe2O3'] >= DataCalculating['Cl']:
ACorN = 'Acmite'
else:
if DataCalculating['Fe2O3'] > 0:
ACorN = 'Both'
else:
ACorN = 'Na2SiO3'
if ACorN == 'Acmite':
DataCalculating['F'] = DataCalculating['Cl']
DataCalculating['Cl'] = 0
elif ACorN == 'Na2SiO3':
DataCalculating['Cl'] = DataCalculating['Cl']
DataCalculating['F'] = 0
elif ACorN == 'Both':
DataCalculating['F'] = DataCalculating['Fe2O3']
DataCalculating['Cl'] = DataCalculating['Cl'] - DataCalculating['F']
elif ACorN == 'None':
DataCalculating['F'] = 0
DataCalculating['Cl'] = 0
DataCalculating['Fe2O3'] -= DataCalculating['F']
Acmite = DataCalculating['F']
if DataCalculating['Fe2O3'] <= 0:
MorH = 'None'
else:
if DataCalculating['FeO'] >= DataCalculating['Fe2O3']:
MorH = 'Magnetite'
else:
if DataCalculating['FeO'] > 0:
MorH = 'Both'
else:
MorH = 'Hematite'
if MorH == 'Magnetite':
DataCalculating['Fe2O3'] = DataCalculating['Fe2O3']
DataCalculating['Ba'] = 0
elif MorH == 'Hematite':
DataCalculating['Fe2O3'] = 0
DataCalculating['Ba'] = DataCalculating['FeO']
elif MorH == 'Both':
DataCalculating['Fe2O3'] = DataCalculating['FeO']
DataCalculating['Ba'] = DataCalculating['Fe2O3'] - DataCalculating['FeO']
elif MorH == 'None':
DataCalculating['Fe2O3'] = 0
DataCalculating['Ba'] == 0
DataCalculating['FeO'] -= DataCalculating['Fe2O3']
Magnetite = DataCalculating['Fe2O3']
Hematite = DataCalculating['Ba']
Fe2 = DataCalculating['FeO']
Mg = DataCalculating['MgO']
if Mg > 0:
DataCalced.update({'Mg/(Mg+Fe2+) in silicates': 100 * Mg / (Mg + Fe2)})
else:
DataCalced.update({'Mg/(Mg+Fe2+) in silicates': 0})
DataCalculating['FeO'] += DataCalculating['MgO']
DataCalculating['MgO'] = 0
if DataCalculating['CaO'] <= 0:
DorW = 'None'
else:
if DataCalculating['FeO'] >= DataCalculating['CaO']:
DorW = 'Diopside'
else:
if DataCalculating['FeO'] > 0:
DorW = 'Both'
else:
DorW = 'Wollastonite'
if DorW == 'Diopside':
DataCalculating['CaO'] = DataCalculating['CaO']
DataCalculating['S'] = 0
elif DorW == 'Wollastonite':
DataCalculating['S'] = DataCalculating['CaO']
DataCalculating['CaO'] = 0
elif DorW == 'Both':
DataCalculating['S'] = DataCalculating['CaO'] - DataCalculating['FeO']
DataCalculating['CaO'] = DataCalculating['FeO']
elif DorW == 'None':
DataCalculating['CaO'] = 0
DataCalculating['S'] = 0
DataCalculating['FeO'] -= DataCalculating['CaO']
Diopside = DataCalculating['CaO']
Quartz = DataCalculating['SiO2']
Zircon = DataCalculating['Zr']
K2SiO3 = DataCalculating['P2O5']
Na2SiO3 = DataCalculating['Cl']
Sphene = DataCalculating['MnO']
Hypersthene = DataCalculating['FeO']
Albite = DataCalculating['Na2O']
Orthoclase = DataCalculating['K2O']
Wollastonite = DataCalculating['S']
Quartz -= (Zircon +
K2SiO3 +
Anorthite * 2 +
Na2SiO3 +
Acmite * 4 +
Diopside * 2 +
Sphene +
Hypersthene +
Albite * 6 +
Orthoclase * 6 +
Wollastonite)
if Quartz > 0:
Quartz = Quartz
else:
Quartz = 0
if Hypersthene <= 0:
HorO = 'None'
else:
if Quartz >= 0:
HorO = 'Hypersthene'
else:
if Hypersthene + 2 * Quartz > 0:
HorO = 'Both'
else:
HorO = 'Olivine'
Old_Hypersthene = Hypersthene
if HorO == 'Hypersthene':
Hypersthene = Hypersthene
Olivine = 0
elif HorO == 'Both':
Hypersthene = Hypersthene + Quartz * 2
Olivine = abs(Quartz)
elif HorO == 'Olivine':
Olivine = Hypersthene / 2
Hypersthene = 0
elif HorO == 'None':
Hypersthene = 0
Olivine = 0
Quartz += Old_Hypersthene - (Hypersthene + Olivine)
if Sphene <= 0:
SorP = 'None'
else:
if Quartz >= 0:
SorP = 'Sphene'
else:
if Sphene + Quartz > 0:
SorP = 'Both'
else:
SorP = 'Perovskite'
Old_Sphene = Sphene
if SorP == 'Sphene':
Sphene = Sphene
Perovskite = 0
elif SorP == 'Perovskite':
Perovskite = Sphene
Sphene = 0
elif SorP == 'Both':
Sphene += Quartz
Perovskite = Old_Sphene - Sphene
elif SorP == 'None':
Sphene = 0
Perovskite = 0
Quartz += Old_Sphene - Sphene
if Albite <= 0:
AlorNe = 'None'
else:
if Quartz >= 0:
AlorNe = 'Albite'
else:
if Albite + (Quartz / 4) > 0:
AlorNe = 'Both'
else:
AlorNe = 'Nepheline'
Old_Albite = Albite
if AlorNe == 'Albite':
Albite = Albite
Nepheline = 0
elif AlorNe == 'Nepheline':
Nepheline = Albite
Albite = 0
elif AlorNe == 'Both':
Albite += Quartz / 4
Nepheline = Old_Albite - Albite
elif AlorNe == 'None':
Nepheline = 0
Albite = 0
Quartz += (6 * Old_Albite) - (Albite * 6) - (Nepheline * 2)
if Anorthite == 0:
DataCalced.update({'Plagioclase An content': 0})
else:
DataCalced.update({'Plagioclase An content': 100 * Anorthite / (Anorthite + 2 * Albite)})
if Orthoclase <= 0:
OorL = 'None'
else:
if Quartz >= 0:
OorL = 'Orthoclase'
else:
if Orthoclase + Quartz / 2 > 0:
OorL = 'Both'
else:
OorL = 'Leucite'
Old_Orthoclase = Orthoclase
if OorL == 'Orthoclase':
Orthoclase = Orthoclase
Leucite = 0
elif OorL == 'Leucite':
Leucite = Orthoclase
Orthoclase = 0
elif OorL == 'Both':
Orthoclase += Quartz / 2
Leucite = Old_Orthoclase - Orthoclase
elif OorL == 'None':
Orthoclase = 0
Leucite = 0
Quartz += (Old_Orthoclase * 6) - (Orthoclase * 6) - (Leucite * 4)
if Wollastonite <= 0:
WorB = 'None'
else:
if Quartz >= 0:
WorB = 'Wollastonite'
else:
if Wollastonite + Quartz / 2 > 0:
WorB = 'Both'
else:
WorB = 'Larnite'
Old_Wollastonite = Wollastonite
if WorB == 'Wollastonite':
Wollastonite = Wollastonite
Larnite = 0
elif WorB == 'Larnite':
Larnite = Wollastonite / 2
Wollastonite = 0
elif WorB == 'Both':
Wollastonite += Quartz * 2
Larnite = (Old_Wollastonite - Wollastonite) / 2
elif WorB == 'None':
Wollastonite = 0
Larnite = 0
Quartz += Old_Wollastonite - Wollastonite - Larnite
if Diopside <= 0:
DorL = 'None'
else:
if Quartz >= 0:
DorL = 'Diopside'
else:
if Diopside + Quartz > 0:
DorL = 'Both'
else:
DorL = 'LarniteOlivine'
Old_Diopside = Diopside
Old_Larnite = Larnite
Old_Olivine = Olivine
if DorL == 'Diopside':
Diopside = Diopside
elif DorL == 'LarniteOlivine':
Larnite += Diopside / 2
Olivine += Diopside / 2
Diopside = 0
elif DorL == 'Both':
Diopside += Quartz
Larnite += Old_Diopside - Diopside
Olivine += Old_Diopside - Diopside
elif DorL == 'None':
Diopside = 0
Quartz += (Old_Diopside * 2) + Old_Olivine + Old_Larnite - Larnite - (Diopside * 2) - Olivine
if Leucite <= 0:
LorK = 'None'
else:
if Quartz >= 0:
LorK = 'Leucite'
else:
if Leucite + Quartz / 2 > 0:
LorK = 'Both'
else:
LorK = 'Kalsilite'
Old_Leucite = Leucite
if LorK == 'Leucite':
Leucite = Leucite
Kalsilite = 0
elif LorK == 'Kalsilite':
Kalsilite = Leucite
Leucite = 0
elif LorK == 'Both':
Leucite += Quartz / 2
Kalsilite = Old_Leucite - Leucite
elif LorK == 'None':
Leucite = 0
Kalsilite = 0
Quartz += Old_Leucite * 4 - Leucite * 4 - Kalsilite * 2
Q = Quartz
A = Orthoclase
P = Anorthite + Albite
F = Nepheline + Leucite + Kalsilite
DataResult.update({'Quartz': Quartz})
DataResult.update({'Zircon': Zircon})
DataResult.update({'K2SiO3': K2SiO3})
DataResult.update({'Anorthite': Anorthite})
DataResult.update({'Na2SiO3': Na2SiO3})
DataResult.update({'Acmite': Acmite})
DataResult.update({'Diopside': Diopside})
DataResult.update({'Sphene': Sphene})
DataResult.update({'Hypersthene': Hypersthene})
DataResult.update({'Albite': Albite})
DataResult.update({'Orthoclase': Orthoclase})
DataResult.update({'Wollastonite': Wollastonite})
DataResult.update({'Olivine': Olivine})
DataResult.update({'Perovskite': Perovskite})
DataResult.update({'Nepheline': Nepheline})
DataResult.update({'Leucite': Leucite})
DataResult.update({'Larnite': Larnite})
DataResult.update({'Kalsilite': Kalsilite})
DataResult.update({'Apatite': Apatite})
DataResult.update({'Halite': Halite})
DataResult.update({'Fluorite': Fluorite})
DataResult.update({'Anhydrite': Anhydrite})
DataResult.update({'Thenardite': Thenardite})
DataResult.update({'Pyrite': Pyrite})
DataResult.update({'Magnesiochromite': Magnesiochromite})
DataResult.update({'Chromite': Chromite})
DataResult.update({'Ilmenite': Ilmenite})
DataResult.update({'Calcite': Calcite})
DataResult.update({'Na2CO3': Na2CO3})
DataResult.update({'Corundum': Corundum})
DataResult.update({'Rutile': Rutile})
DataResult.update({'Magnetite': Magnetite})
DataResult.update({'Hematite': Hematite})
DataResult.update({'Q Mole': Q})
DataResult.update({'A Mole': A})
DataResult.update({'P Mole': P})
DataResult.update({'F Mole': F})
DataWeight.update({'Quartz': Quartz * self.DataBase['Quartz'][0]})
DataWeight.update({'Zircon': Zircon * self.DataBase['Zircon'][0]})
DataWeight.update({'K2SiO3': K2SiO3 * self.DataBase['K2SiO3'][0]})
DataWeight.update({'Anorthite': Anorthite * self.DataBase['Anorthite'][0]})
DataWeight.update({'Na2SiO3': Na2SiO3 * self.DataBase['Na2SiO3'][0]})
DataWeight.update({'Acmite': Acmite * self.DataBase['Acmite'][0]})
DataWeight.update({'Diopside': Diopside * self.DataBase['Diopside'][0]})
DataWeight.update({'Sphene': Sphene * self.DataBase['Sphene'][0]})
DataWeight.update({'Hypersthene': Hypersthene * self.DataBase['Hypersthene'][0]})
DataWeight.update({'Albite': Albite * self.DataBase['Albite'][0]})
DataWeight.update({'Orthoclase': Orthoclase * self.DataBase['Orthoclase'][0]})
DataWeight.update({'Wollastonite': Wollastonite * self.DataBase['Wollastonite'][0]})
DataWeight.update({'Olivine': Olivine * self.DataBase['Olivine'][0]})
DataWeight.update({'Perovskite': Perovskite * self.DataBase['Perovskite'][0]})
DataWeight.update({'Nepheline': Nepheline * self.DataBase['Nepheline'][0]})
DataWeight.update({'Leucite': Leucite * self.DataBase['Leucite'][0]})
DataWeight.update({'Larnite': Larnite * self.DataBase['Larnite'][0]})
DataWeight.update({'Kalsilite': Kalsilite * self.DataBase['Kalsilite'][0]})
DataWeight.update({'Apatite': Apatite * self.DataBase['Apatite'][0]})
DataWeight.update({'Halite': Halite * self.DataBase['Halite'][0]})
DataWeight.update({'Fluorite': Fluorite * self.DataBase['Fluorite'][0]})
DataWeight.update({'Anhydrite': Anhydrite * self.DataBase['Anhydrite'][0]})
DataWeight.update({'Thenardite': Thenardite * self.DataBase['Thenardite'][0]})
DataWeight.update({'Pyrite': Pyrite * self.DataBase['Pyrite'][0]})
DataWeight.update({'Magnesiochromite': Magnesiochromite * self.DataBase['Magnesiochromite'][0]})
DataWeight.update({'Chromite': Chromite * self.DataBase['Chromite'][0]})
DataWeight.update({'Ilmenite': Ilmenite * self.DataBase['Ilmenite'][0]})
DataWeight.update({'Calcite': Calcite * self.DataBase['Calcite'][0]})
DataWeight.update({'Na2CO3': Na2CO3 * self.DataBase['Na2CO3'][0]})
DataWeight.update({'Corundum': Corundum * self.DataBase['Corundum'][0]})
DataWeight.update({'Rutile': Rutile * self.DataBase['Rutile'][0]})
DataWeight.update({'Magnetite': Magnetite * self.DataBase['Magnetite'][0]})
DataWeight.update({'Hematite': Hematite * self.DataBase['Hematite'][0]})
DataWeight.update({'Q Weight': Quartz * self.DataBase['Quartz'][0]})
DataWeight.update({'A Weight': Orthoclase * self.DataBase['Orthoclase'][0]})
DataWeight.update({'P Weight': Anorthite * self.DataBase['Anorthite'][0] + Albite * self.DataBase['Albite'][0]})
DataWeight.update({'F Weight': Nepheline * self.DataBase['Nepheline'][0] + Leucite * self.DataBase['Leucite'][0] + Kalsilite * self.DataBase['Kalsilite'][0]})
WholeVolume = 0
WholeMole = 0
tmpVolume = []
tmpVolume.append(Quartz * self.DataBase['Quartz'][0] / self.DataBase['Quartz'][1])
tmpVolume.append(Zircon * self.DataBase['Zircon'][0] / self.DataBase['Zircon'][1])
tmpVolume.append(K2SiO3 * self.DataBase['K2SiO3'][0] / self.DataBase['K2SiO3'][1])
tmpVolume.append(Anorthite * self.DataBase['Anorthite'][0] / self.DataBase['Anorthite'][1])
tmpVolume.append(Na2SiO3 * self.DataBase['Na2SiO3'][0] / self.DataBase['Na2SiO3'][1])
tmpVolume.append(Acmite * self.DataBase['Acmite'][0] / self.DataBase['Acmite'][1])
tmpVolume.append(Diopside * self.DataBase['Diopside'][0] / self.DataBase['Diopside'][1])
tmpVolume.append(Sphene * self.DataBase['Sphene'][0] / self.DataBase['Sphene'][1])
tmpVolume.append(Hypersthene * self.DataBase['Hypersthene'][0] / self.DataBase['Hypersthene'][1])
tmpVolume.append(Albite * self.DataBase['Albite'][0] / self.DataBase['Albite'][1])
tmpVolume.append(Orthoclase * self.DataBase['Orthoclase'][0] / self.DataBase['Orthoclase'][1])
tmpVolume.append(Wollastonite * self.DataBase['Wollastonite'][0] / self.DataBase['Wollastonite'][1])
tmpVolume.append(Olivine * self.DataBase['Olivine'][0] / self.DataBase['Olivine'][1])
tmpVolume.append(Perovskite * self.DataBase['Perovskite'][0] / self.DataBase['Perovskite'][1])
tmpVolume.append(Nepheline * self.DataBase['Nepheline'][0] / self.DataBase['Nepheline'][1])
tmpVolume.append(Leucite * self.DataBase['Leucite'][0] / self.DataBase['Leucite'][1])
tmpVolume.append(Larnite * self.DataBase['Larnite'][0] / self.DataBase['Larnite'][1])
tmpVolume.append(Kalsilite * self.DataBase['Kalsilite'][0] / self.DataBase['Kalsilite'][1])
tmpVolume.append(Apatite * self.DataBase['Apatite'][0] / self.DataBase['Apatite'][1])
tmpVolume.append(Halite * self.DataBase['Halite'][0] / self.DataBase['Halite'][1])
tmpVolume.append(Fluorite * self.DataBase['Fluorite'][0] / self.DataBase['Fluorite'][1])
tmpVolume.append(Anhydrite * self.DataBase['Anhydrite'][0] / self.DataBase['Anhydrite'][1])
tmpVolume.append(Thenardite * self.DataBase['Thenardite'][0] / self.DataBase['Thenardite'][1])
tmpVolume.append(Pyrite * self.DataBase['Pyrite'][0] / self.DataBase['Pyrite'][1])
tmpVolume.append(Magnesiochromite * self.DataBase['Magnesiochromite'][0] / self.DataBase['Magnesiochromite'][1])
tmpVolume.append(Chromite * self.DataBase['Chromite'][0] / self.DataBase['Chromite'][1])
tmpVolume.append(Ilmenite * self.DataBase['Ilmenite'][0] / self.DataBase['Ilmenite'][1])
tmpVolume.append(Calcite * self.DataBase['Calcite'][0] / self.DataBase['Calcite'][1])
tmpVolume.append(Na2CO3 * self.DataBase['Na2CO3'][0] / self.DataBase['Na2CO3'][1])
tmpVolume.append(Corundum * self.DataBase['Corundum'][0] / self.DataBase['Corundum'][1])
tmpVolume.append(Rutile * self.DataBase['Rutile'][0] / self.DataBase['Rutile'][1])
tmpVolume.append(Magnetite * self.DataBase['Magnetite'][0] / self.DataBase['Magnetite'][1])
tmpVolume.append(Hematite * self.DataBase['Hematite'][0] / self.DataBase['Hematite'][1])
WholeVolume = sum(tmpVolume)
DataVolume.update(
{'Quartz': (Quartz * self.DataBase['Quartz'][0] / self.DataBase['Quartz'][1]) / WholeVolume * 100})
DataVolume.update(
{'Zircon': (Zircon * self.DataBase['Zircon'][0] / self.DataBase['Zircon'][1]) / WholeVolume * 100})
DataVolume.update(
{'K2SiO3': (K2SiO3 * self.DataBase['K2SiO3'][0] / self.DataBase['K2SiO3'][1]) / WholeVolume * 100})
DataVolume.update({'Anorthite': (Anorthite * self.DataBase['Anorthite'][0] / self.DataBase['Anorthite'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Na2SiO3': (Na2SiO3 * self.DataBase['Na2SiO3'][0] / self.DataBase['Na2SiO3'][1]) / WholeVolume * 100})
DataVolume.update(
{'Acmite': (Acmite * self.DataBase['Acmite'][0] / self.DataBase['Acmite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Diopside': (Diopside * self.DataBase['Diopside'][0] / self.DataBase['Diopside'][1]) / WholeVolume * 100})
DataVolume.update(
{'Sphene': (Sphene * self.DataBase['Sphene'][0] / self.DataBase['Sphene'][1]) / WholeVolume * 100})
DataVolume.update({'Hypersthene': (Hypersthene * self.DataBase['Hypersthene'][0] / self.DataBase['Hypersthene'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Albite': (Albite * self.DataBase['Albite'][0] / self.DataBase['Albite'][1]) / WholeVolume * 100})
DataVolume.update({'Orthoclase': (Orthoclase * self.DataBase['Orthoclase'][0] / self.DataBase['Orthoclase'][
1]) / WholeVolume * 100})
DataVolume.update({'Wollastonite': (Wollastonite * self.DataBase['Wollastonite'][0] /
self.DataBase['Wollastonite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Olivine': (Olivine * self.DataBase['Olivine'][0] / self.DataBase['Olivine'][1]) / WholeVolume * 100})
DataVolume.update({'Perovskite': (Perovskite * self.DataBase['Perovskite'][0] / self.DataBase['Perovskite'][
1]) / WholeVolume * 100})
DataVolume.update({'Nepheline': (Nepheline * self.DataBase['Nepheline'][0] / self.DataBase['Nepheline'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Leucite': (Leucite * self.DataBase['Leucite'][0] / self.DataBase['Leucite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Larnite': (Larnite * self.DataBase['Larnite'][0] / self.DataBase['Larnite'][1]) / WholeVolume * 100})
DataVolume.update({'Kalsilite': (Kalsilite * self.DataBase['Kalsilite'][0] / self.DataBase['Kalsilite'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Apatite': (Apatite * self.DataBase['Apatite'][0] / self.DataBase['Apatite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Halite': (Halite * self.DataBase['Halite'][0] / self.DataBase['Halite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Fluorite': (Fluorite * self.DataBase['Fluorite'][0] / self.DataBase['Fluorite'][1]) / WholeVolume * 100})
DataVolume.update({'Anhydrite': (Anhydrite * self.DataBase['Anhydrite'][0] / self.DataBase['Anhydrite'][
1]) / WholeVolume * 100})
DataVolume.update({'Thenardite': (Thenardite * self.DataBase['Thenardite'][0] / self.DataBase['Thenardite'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Pyrite': (Pyrite * self.DataBase['Pyrite'][0] / self.DataBase['Pyrite'][1]) / WholeVolume * 100})
DataVolume.update({'Magnesiochromite': (Magnesiochromite * self.DataBase['Magnesiochromite'][0] /
self.DataBase['Magnesiochromite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Chromite': (Chromite * self.DataBase['Chromite'][0] / self.DataBase['Chromite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Ilmenite': (Ilmenite * self.DataBase['Ilmenite'][0] / self.DataBase['Ilmenite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Calcite': (Calcite * self.DataBase['Calcite'][0] / self.DataBase['Calcite'][1]) / WholeVolume * 100})
DataVolume.update(
{'Na2CO3': (Na2CO3 * self.DataBase['Na2CO3'][0] / self.DataBase['Na2CO3'][1]) / WholeVolume * 100})
DataVolume.update(
{'Corundum': (Corundum * self.DataBase['Corundum'][0] / self.DataBase['Corundum'][1]) / WholeVolume * 100})
DataVolume.update(
{'Rutile': (Rutile * self.DataBase['Rutile'][0] / self.DataBase['Rutile'][1]) / WholeVolume * 100})
DataVolume.update({'Magnetite': (Magnetite * self.DataBase['Magnetite'][0] / self.DataBase['Magnetite'][
1]) / WholeVolume * 100})
DataVolume.update(
{'Hematite': (Hematite * self.DataBase['Hematite'][0] / self.DataBase['Hematite'][1]) / WholeVolume * 100})
DataVolume.update({'Q': DataVolume['Quartz']})
DataVolume.update({'A': DataVolume['Orthoclase']})
DataVolume.update({'P': DataVolume['Anorthite'] + DataVolume['Albite']})
DataVolume.update({'F': DataVolume['Nepheline'] + DataVolume['Leucite'] + DataVolume['Kalsilite']})
DI = 0
DI = Quartz + Anorthite + Albite + Orthoclase + Nepheline + Leucite + Kalsilite
DiWeight=0
DiVolume=0
DiWeight = DataWeight['Quartz']+DataWeight['Anorthite']+DataWeight['Albite']+DataWeight['Orthoclase']+DataWeight['Nepheline']+DataWeight['Leucite']+DataWeight['Kalsilite']
DiVolume = DataVolume['Quartz']+DataVolume['Anorthite']+DataVolume['Albite']+DataVolume['Orthoclase']+DataVolume['Nepheline']+DataVolume['Leucite']+DataVolume['Kalsilite']
DataCalced.update({'Differentiation Index Weight': DiWeight})
DataCalced.update({'Differentiation Index Volume': DiVolume})
return (DataResult, DataWeight, DataVolume, DataCalced) | [
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":",
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"[",
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":",
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"[",
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":",
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":",
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"'Na2SiO3'",
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"[",
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"[",
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":",
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"[",
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"]",
"-=",
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"[",
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"-",
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"[",
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"[",
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"[",
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"[",
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"0",
":",
"AorC",
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":",
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":",
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":",
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"[",
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"[",
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"-=",
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"[",
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"]",
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"[",
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"[",
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"]",
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"[",
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"-=",
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"[",
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"[",
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"]",
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"[",
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"0",
"Corundum",
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":",
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":",
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"[",
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":",
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":",
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":",
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"[",
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":",
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":",
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":",
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"[",
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":",
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":",
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":",
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":",
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"=",
"0",
"elif",
"DorW",
"==",
"'Both'",
":",
"DataCalculating",
"[",
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"]",
"=",
"DataCalculating",
"[",
"'CaO'",
"]",
"-",
"DataCalculating",
"[",
"'FeO'",
"]",
"DataCalculating",
"[",
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"]",
"=",
"DataCalculating",
"[",
"'FeO'",
"]",
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"==",
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":",
"DataCalculating",
"[",
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"]",
"=",
"0",
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"[",
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"]",
"=",
"0",
"DataCalculating",
"[",
"'FeO'",
"]",
"-=",
"DataCalculating",
"[",
"'CaO'",
"]",
"Diopside",
"=",
"DataCalculating",
"[",
"'CaO'",
"]",
"Quartz",
"=",
"DataCalculating",
"[",
"'SiO2'",
"]",
"Zircon",
"=",
"DataCalculating",
"[",
"'Zr'",
"]",
"K2SiO3",
"=",
"DataCalculating",
"[",
"'P2O5'",
"]",
"Na2SiO3",
"=",
"DataCalculating",
"[",
"'Cl'",
"]",
"Sphene",
"=",
"DataCalculating",
"[",
"'MnO'",
"]",
"Hypersthene",
"=",
"DataCalculating",
"[",
"'FeO'",
"]",
"Albite",
"=",
"DataCalculating",
"[",
"'Na2O'",
"]",
"Orthoclase",
"=",
"DataCalculating",
"[",
"'K2O'",
"]",
"Wollastonite",
"=",
"DataCalculating",
"[",
"'S'",
"]",
"# =AJ5-(AL6)-(AL7)-(AL8*2)-(AL12)-(AL9)-(AL10*4)-(AL11*2)-(AL13)-(AL14*6)-(AL15*6)-(AL16)",
"Quartz",
"-=",
"(",
"Zircon",
"+",
"K2SiO3",
"+",
"Anorthite",
"*",
"2",
"+",
"Na2SiO3",
"+",
"Acmite",
"*",
"4",
"+",
"Diopside",
"*",
"2",
"+",
"Sphene",
"+",
"Hypersthene",
"+",
"Albite",
"*",
"6",
"+",
"Orthoclase",
"*",
"6",
"+",
"Wollastonite",
")",
"# =IF(AL5>0,AL5,0)",
"if",
"Quartz",
">",
"0",
":",
"Quartz",
"=",
"Quartz",
"else",
":",
"Quartz",
"=",
"0",
"# =IF(AL13>0,IF(AL5>=0,'Hypersthene',IF(AL13+(2*AL5)>0,'Both','Olivine')),'None')",
"if",
"Hypersthene",
"<=",
"0",
":",
"HorO",
"=",
"'None'",
"else",
":",
"if",
"Quartz",
">=",
"0",
":",
"HorO",
"=",
"'Hypersthene'",
"else",
":",
"if",
"Hypersthene",
"+",
"2",
"*",
"Quartz",
">",
"0",
":",
"HorO",
"=",
"'Both'",
"else",
":",
"HorO",
"=",
"'Olivine'",
"# =IF(AN26='Hypersthene',AL13,IF(AN26='Both',AL13+(2*AL5),0))",
"# =IF(AN26='Olivine',AL13*0.5,IF(AN26='Both',ABS(AL5),0))",
"Old_Hypersthene",
"=",
"Hypersthene",
"if",
"HorO",
"==",
"'Hypersthene'",
":",
"Hypersthene",
"=",
"Hypersthene",
"Olivine",
"=",
"0",
"elif",
"HorO",
"==",
"'Both'",
":",
"Hypersthene",
"=",
"Hypersthene",
"+",
"Quartz",
"*",
"2",
"Olivine",
"=",
"abs",
"(",
"Quartz",
")",
"elif",
"HorO",
"==",
"'Olivine'",
":",
"Olivine",
"=",
"Hypersthene",
"/",
"2",
"Hypersthene",
"=",
"0",
"elif",
"HorO",
"==",
"'None'",
":",
"Hypersthene",
"=",
"0",
"Olivine",
"=",
"0",
"# =AL5+AL13-(AN13+AN17)",
"Quartz",
"+=",
"Old_Hypersthene",
"-",
"(",
"Hypersthene",
"+",
"Olivine",
")",
"# =IF(AL12>0,IF(AN5>=0,'Sphene',IF(AL12+AN5>0,'Both','Perovskite')),'None')",
"if",
"Sphene",
"<=",
"0",
":",
"SorP",
"=",
"'None'",
"else",
":",
"if",
"Quartz",
">=",
"0",
":",
"SorP",
"=",
"'Sphene'",
"else",
":",
"if",
"Sphene",
"+",
"Quartz",
">",
"0",
":",
"SorP",
"=",
"'Both'",
"else",
":",
"SorP",
"=",
"'Perovskite'",
"# =IF(AO26='Sphene',AL12,IF(AO26='Both',AL12+AN5,0))",
"# =IF(AO26='Perovskite',AL12,IF(AO26='Both',AL12-AO12,0))",
"Old_Sphene",
"=",
"Sphene",
"if",
"SorP",
"==",
"'Sphene'",
":",
"Sphene",
"=",
"Sphene",
"Perovskite",
"=",
"0",
"elif",
"SorP",
"==",
"'Perovskite'",
":",
"Perovskite",
"=",
"Sphene",
"Sphene",
"=",
"0",
"elif",
"SorP",
"==",
"'Both'",
":",
"Sphene",
"+=",
"Quartz",
"Perovskite",
"=",
"Old_Sphene",
"-",
"Sphene",
"elif",
"SorP",
"==",
"'None'",
":",
"Sphene",
"=",
"0",
"Perovskite",
"=",
"0",
"Quartz",
"+=",
"Old_Sphene",
"-",
"Sphene",
"# =IF(AL14>0,IF(AO5>=0,'Albite',IF(AL14+(AO5/4)>0,'Both','Nepheline')),'None')",
"if",
"Albite",
"<=",
"0",
":",
"AlorNe",
"=",
"'None'",
"else",
":",
"if",
"Quartz",
">=",
"0",
":",
"AlorNe",
"=",
"'Albite'",
"else",
":",
"if",
"Albite",
"+",
"(",
"Quartz",
"/",
"4",
")",
">",
"0",
":",
"AlorNe",
"=",
"'Both'",
"else",
":",
"AlorNe",
"=",
"'Nepheline'",
"# =AO5+(6*AL14)-(AP14*6)-(AP19*2)",
"# =IF(AP26='Albite',AL14,IF(AP26='Both',AL14+(AO5/4),0))",
"# =IF(AP26='Nepheline',AL14,IF(AP26='Both',AL14-AP14,0))",
"Old_Albite",
"=",
"Albite",
"if",
"AlorNe",
"==",
"'Albite'",
":",
"Albite",
"=",
"Albite",
"Nepheline",
"=",
"0",
"elif",
"AlorNe",
"==",
"'Nepheline'",
":",
"Nepheline",
"=",
"Albite",
"Albite",
"=",
"0",
"elif",
"AlorNe",
"==",
"'Both'",
":",
"Albite",
"+=",
"Quartz",
"/",
"4",
"Nepheline",
"=",
"Old_Albite",
"-",
"Albite",
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"AlorNe",
"==",
"'None'",
":",
"Nepheline",
"=",
"0",
"Albite",
"=",
"0",
"Quartz",
"+=",
"(",
"6",
"*",
"Old_Albite",
")",
"-",
"(",
"Albite",
"*",
"6",
")",
"-",
"(",
"Nepheline",
"*",
"2",
")",
"# =IF(AL8=0,0,AL8/(AL8+(AP14*2)))",
"if",
"Anorthite",
"==",
"0",
":",
"DataCalced",
".",
"update",
"(",
"{",
"'Plagioclase An content'",
":",
"0",
"}",
")",
"else",
":",
"DataCalced",
".",
"update",
"(",
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"'Plagioclase An content'",
":",
"100",
"*",
"Anorthite",
"/",
"(",
"Anorthite",
"+",
"2",
"*",
"Albite",
")",
"}",
")",
"# =IF(AL15>0,IF(AP5>=0,'Orthoclase',IF(AL15+(AP5/2)>0,'Both','Leucite')),'None')",
"if",
"Orthoclase",
"<=",
"0",
":",
"OorL",
"=",
"'None'",
"else",
":",
"if",
"Quartz",
">=",
"0",
":",
"OorL",
"=",
"'Orthoclase'",
"else",
":",
"if",
"Orthoclase",
"+",
"Quartz",
"/",
"2",
">",
"0",
":",
"OorL",
"=",
"'Both'",
"else",
":",
"OorL",
"=",
"'Leucite'",
"# =IF(AQ26='Orthoclase',AL15,IF(AQ26='Both',AL15+(AP5/2),0))",
"# =IF(AQ26='Leucite',AL15,IF(AQ26='Both',AL15-AQ15,0))",
"Old_Orthoclase",
"=",
"Orthoclase",
"if",
"OorL",
"==",
"'Orthoclase'",
":",
"Orthoclase",
"=",
"Orthoclase",
"Leucite",
"=",
"0",
"elif",
"OorL",
"==",
"'Leucite'",
":",
"Leucite",
"=",
"Orthoclase",
"Orthoclase",
"=",
"0",
"elif",
"OorL",
"==",
"'Both'",
":",
"Orthoclase",
"+=",
"Quartz",
"/",
"2",
"Leucite",
"=",
"Old_Orthoclase",
"-",
"Orthoclase",
"elif",
"OorL",
"==",
"'None'",
":",
"Orthoclase",
"=",
"0",
"Leucite",
"=",
"0",
"# =AP5+(AL15*6)-(AQ15*6)-(AQ20*4)",
"Quartz",
"+=",
"(",
"Old_Orthoclase",
"*",
"6",
")",
"-",
"(",
"Orthoclase",
"*",
"6",
")",
"-",
"(",
"Leucite",
"*",
"4",
")",
"# =IF(AL16>0,IF(AQ5>=0,'Wollastonite',IF(AL16+(AQ5*2)>0,'Both','Larnite')),'None')",
"if",
"Wollastonite",
"<=",
"0",
":",
"WorB",
"=",
"'None'",
"else",
":",
"if",
"Quartz",
">=",
"0",
":",
"WorB",
"=",
"'Wollastonite'",
"else",
":",
"if",
"Wollastonite",
"+",
"Quartz",
"/",
"2",
">",
"0",
":",
"WorB",
"=",
"'Both'",
"else",
":",
"WorB",
"=",
"'Larnite'",
"# =IF(AR26='Wollastonite',AL16,IF(AR26='Both',AL16+(2*AQ5),0))",
"# =IF(AR26='Larnite',AL16/2,IF(AR26='Both',(AL16-AR16)/2,0))",
"Old_Wollastonite",
"=",
"Wollastonite",
"if",
"WorB",
"==",
"'Wollastonite'",
":",
"Wollastonite",
"=",
"Wollastonite",
"Larnite",
"=",
"0",
"elif",
"WorB",
"==",
"'Larnite'",
":",
"Larnite",
"=",
"Wollastonite",
"/",
"2",
"Wollastonite",
"=",
"0",
"elif",
"WorB",
"==",
"'Both'",
":",
"Wollastonite",
"+=",
"Quartz",
"*",
"2",
"Larnite",
"=",
"(",
"Old_Wollastonite",
"-",
"Wollastonite",
")",
"/",
"2",
"elif",
"WorB",
"==",
"'None'",
":",
"Wollastonite",
"=",
"0",
"Larnite",
"=",
"0",
"# =AQ5+AL16-AR16-AR21",
"Quartz",
"+=",
"Old_Wollastonite",
"-",
"Wollastonite",
"-",
"Larnite",
"# =IF(AL11>0,IF(AR5>=0,'Diopside',IF(AL11+AR5>0,'Both','LarniteOlivine')),'None')",
"if",
"Diopside",
"<=",
"0",
":",
"DorL",
"=",
"'None'",
"else",
":",
"if",
"Quartz",
">=",
"0",
":",
"DorL",
"=",
"'Diopside'",
"else",
":",
"if",
"Diopside",
"+",
"Quartz",
">",
"0",
":",
"DorL",
"=",
"'Both'",
"else",
":",
"DorL",
"=",
"'LarniteOlivine'",
"# =IF(AS26='Diopside',AL11,IF(AS26='Both',AL11+AR5,0))",
"# =(IF(AS26='LarniteOlivine',AL11/2,IF(AS26='Both',(AL11-AS11)/2,0)))+AN17",
"# =(IF(AS26='LarniteOlivine',AL11/2,IF(AS26='Both',(AL11-AS11)/2,0)))+AR21",
"Old_Diopside",
"=",
"Diopside",
"Old_Larnite",
"=",
"Larnite",
"Old_Olivine",
"=",
"Olivine",
"if",
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"==",
"'Diopside'",
":",
"Diopside",
"=",
"Diopside",
"elif",
"DorL",
"==",
"'LarniteOlivine'",
":",
"Larnite",
"+=",
"Diopside",
"/",
"2",
"Olivine",
"+=",
"Diopside",
"/",
"2",
"Diopside",
"=",
"0",
"elif",
"DorL",
"==",
"'Both'",
":",
"Diopside",
"+=",
"Quartz",
"Larnite",
"+=",
"Old_Diopside",
"-",
"Diopside",
"Olivine",
"+=",
"Old_Diopside",
"-",
"Diopside",
"elif",
"DorL",
"==",
"'None'",
":",
"Diopside",
"=",
"0",
"# =AR5+(AL11*2)+AN17+AR21-AS21-(AS11*2)-AS17",
"Quartz",
"+=",
"(",
"Old_Diopside",
"*",
"2",
")",
"+",
"Old_Olivine",
"+",
"Old_Larnite",
"-",
"Larnite",
"-",
"(",
"Diopside",
"*",
"2",
")",
"-",
"Olivine",
"# =IF(AQ20>0,IF(AS5>=0,'Leucite',IF(AQ20+(AS5/2)>0,'Both','Kalsilite')),'None')",
"if",
"Leucite",
"<=",
"0",
":",
"LorK",
"=",
"'None'",
"else",
":",
"if",
"Quartz",
">=",
"0",
":",
"LorK",
"=",
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":",
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"+",
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"/",
"2",
">",
"0",
":",
"LorK",
"=",
"'Both'",
"else",
":",
"LorK",
"=",
"'Kalsilite'",
"# =IF(AT26='Leucite',AQ20,IF(AT26='Both',AQ20+(AS5/2),0))",
"# =IF(AT26='Kalsilite',AQ20,IF(AT26='Both',AQ20-AT20,0))",
"Old_Leucite",
"=",
"Leucite",
"if",
"LorK",
"==",
"'Leucite'",
":",
"Leucite",
"=",
"Leucite",
"Kalsilite",
"=",
"0",
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"==",
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":",
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"=",
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"=",
"0",
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":",
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"/",
"2",
"Kalsilite",
"=",
"Old_Leucite",
"-",
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"==",
"'None'",
":",
"Leucite",
"=",
"0",
"Kalsilite",
"=",
"0",
"# =AS5+(AQ20*4)-(AT20*4)-(AT22*2)",
"Quartz",
"+=",
"Old_Leucite",
"*",
"4",
"-",
"Leucite",
"*",
"4",
"-",
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"*",
"2",
"Q",
"=",
"Quartz",
"A",
"=",
"Orthoclase",
"P",
"=",
"Anorthite",
"+",
"Albite",
"F",
"=",
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"+",
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"+",
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"(",
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":",
"Quartz",
"}",
")",
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"update",
"(",
"{",
"'Zircon'",
":",
"Zircon",
"}",
")",
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"(",
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":",
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"}",
")",
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"(",
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":",
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"}",
")",
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"(",
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":",
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"}",
")",
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"(",
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":",
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"}",
")",
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"(",
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":",
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"}",
")",
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"(",
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":",
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"}",
")",
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"(",
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":",
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"}",
")",
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"(",
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":",
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"}",
")",
"DataResult",
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"(",
"{",
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":",
"Orthoclase",
"}",
")",
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"(",
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"'Wollastonite'",
":",
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"}",
")",
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"(",
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"'Olivine'",
":",
"Olivine",
"}",
")",
"DataResult",
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"(",
"{",
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":",
"Perovskite",
"}",
")",
"DataResult",
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"(",
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":",
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"}",
")",
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"(",
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":",
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"}",
")",
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"(",
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":",
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")",
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")",
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")",
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":",
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")",
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"(",
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":",
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")",
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"(",
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":",
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")",
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"(",
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":",
"Thenardite",
"}",
")",
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"(",
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":",
"Pyrite",
"}",
")",
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"(",
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":",
"Magnesiochromite",
"}",
")",
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"(",
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":",
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")",
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"(",
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":",
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")",
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"(",
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"'Calcite'",
":",
"Calcite",
"}",
")",
"DataResult",
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"(",
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"'Na2CO3'",
":",
"Na2CO3",
"}",
")",
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"(",
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"'Corundum'",
":",
"Corundum",
"}",
")",
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"(",
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":",
"Rutile",
"}",
")",
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"(",
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"'Magnetite'",
":",
"Magnetite",
"}",
")",
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"(",
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"'Hematite'",
":",
"Hematite",
"}",
")",
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"(",
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":",
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| Seem like should be =IF(AD7>0,IF(AD12>AD7,'Anorthite','Corundum'),'None')
If Al2O3 is left after alloting orthoclase and albite, then:
Anorthite = Al2O3, CaO = CaO - Al2O3, SiO2 = SiO2 - 2 Al2O3, Al2O3 = 0
If Al2O3 exceeds CaO in the preceding calculation, then:
Anorthite = CaO, Al2O3 = Al2O3 - CaO, SiO2 = SiO2 - 2 CaO
Corundum = Al2O3, CaO =0, Al2O3 = 0
if DataCalculating['Al2O3']<=0:
AorC='None'
else:
if DataCalculating['CaO']>DataCalculating['Al2O3']:
AorC= 'Anorthite'
else:
Aorc='Corundum' | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/NewCIPW.py#L307-L1492 |
GeoPyTool/GeoPyTool | geopytool/__init__.py | Ui_MainWindow.retranslateUi | def retranslateUi(self):
_translate = QtCore.QCoreApplication.translate
self.talk= _translate('MainWindow','You are using GeoPyTool ') + version +'\n'+ _translate('MainWindow','released on ') + date + '\n'
self.menuFile.setTitle(_translate('MainWindow', u'Data File'))
self.menuGeoChem.setTitle(_translate('MainWindow', u'Geochemistry'))
self.menuGeoCalc.setTitle(_translate('MainWindow',u'Calculation'))
self.menuStructure.setTitle(_translate('MainWindow', u'Structure'))
self.menuSedimentary.setTitle(_translate('MainWindow', u'Sedimentary'))
self.menuAdditional.setTitle(_translate('MainWindow', u'Additional Functions'))
self.menuHelp.setTitle(_translate('MainWindow', u'Help'))
self.menuLanguage.setTitle(_translate('MainWindow', u'Language'))
self.actionCombine.setText(_translate('MainWindow', u'Combine'))
self.actionCombine_transverse.setText(_translate('MainWindow', u'Combine_transverse'))
self.actionFlatten.setText(_translate('MainWindow',u'Flatten'))
self.actionTrans.setText(_translate('MainWindow',u'Trans'))
self.actionReFormat.setText(_translate('MainWindow',u'ReFormat'))
self.actionOpen.setText(_translate('MainWindow', u'Open Data'))
self.actionClose.setText(_translate('MainWindow', u'Close Data'))
self.actionSet.setText(_translate('MainWindow', u'Set Format'))
self.actionSave.setText(_translate('MainWindow', u'Save Data'))
self.actionQuit.setText(_translate('MainWindow', u'Quit App'))
self.actionRemoveLOI.setText('1-0 '+_translate('MainWindow',u'Remove LOI'))
self.actionAuto.setText('1-1 '+_translate('MainWindow', u'Auto'))
self.actionTAS.setText('1-2 '+ _translate('MainWindow',u'TAS'))
self.actionTrace.setText('1-3 '+_translate('MainWindow',u'Trace'))
self.actionRee.setText('1-4 '+_translate('MainWindow',u'REE'))
self.actionPearce.setText('1-5 '+_translate('MainWindow',u'Pearce'))
self.actionHarker.setText('1-6 '+_translate('MainWindow',u'Harker'))
self.actionCIPW.setText('1-7 '+_translate('MainWindow',u'CIPW'))
self.actionQAPF.setText('1-8 '+_translate('MainWindow',u'QAPF'))
self.actionSaccani.setText('1-9 '+_translate('MainWindow',u'Saccani Plot'))
self.actionK2OSiO2.setText('1-10 '+_translate('MainWindow',u'K2O-SiO2'))
self.actionRaman.setText('1-11 '+_translate('MainWindow',u'Raman Strength'))
self.actionFluidInclusion.setText('1-12 '+_translate('MainWindow',u'Fluid Inclusion'))
self.actionHarkerOld.setText('1-14 '+_translate('MainWindow',u'Harker Classical'))
self.actionTraceNew.setText('1-15 '+_translate('MainWindow',u'TraceNew'))
self.actionStereo.setText('2-1 '+_translate('MainWindow',u'Stereo'))
self.actionRose.setText('2-2 '+_translate('MainWindow',u'Rose'))
self.actionQFL.setText('3-1 '+_translate('MainWindow',u'QFL'))
self.actionQmFLt.setText('3-2 '+_translate('MainWindow',u'QmFLt'))
self.actionClastic.setText('3-3 '+_translate('MainWindow',u'Clastic'))
self.actionCIA.setText('3-4 '+ _translate('MainWindow',u'CIA and ICV'))
self.actionZirconCe.setText('4-1 '+ _translate('MainWindow',u'ZirconCe'))
self.actionZirconCeOld.setText('4-2 '+ _translate('MainWindow', u'ZirconCeOld'))
self.actionZirconTiTemp.setText('4-3 '+ _translate('MainWindow',u'ZirconTiTemp'))
self.actionRutileZrTemp.setText('4-4 '+_translate('MainWindow',u'RutileZrTemp'))
self.actionRbSrIsoTope.setText('4-5 '+_translate('MainWindow',u'Rb-Sr IsoTope'))
self.actionSmNdIsoTope.setText('4-6 '+_translate('MainWindow',u'Sm-Nd IsoTope'))
#self.actionKArIsoTope.setText(_translate('MainWindow',u'K-Ar IsoTope'))
self.actionXY.setText('5-1 '+_translate('MainWindow',u'X-Y plot'))
self.actionXYZ.setText('5-2 '+_translate('MainWindow',u'X-Y-Z plot'))
self.actionCluster.setText('5-3 '+_translate('MainWindow',u'Cluster'))
self.actionMultiDimension.setText('5-4 '+_translate('MainWindow',u'MultiDimension'))
self.actionFA.setText('5-5 '+_translate('MainWindow',u'FA'))
self.actionPCA.setText('5-6 '+_translate('MainWindow',u'PCA'))
self.actionDist.setText('5-7 '+_translate('MainWindow',u'Distance'))
self.actionStatistics.setText('5-8 '+_translate('MainWindow',u'Statistics'))
self.actionThreeD.setText('5-9 '+_translate('MainWindow',u'ThreeD'))
self.actionTwoD.setText('5-10 '+_translate('MainWindow',u'TwoD'))
self.actionTwoD_Grey.setText('5-11 '+_translate('MainWindow',u'TwoD Grey'))
self.actionMyHist.setText('5-12 '+_translate('MainWindow',u'Histogram + KDE Curve'))
self.actionVersionCheck.setText(_translate('MainWindow', u'Check Update'))
self.actionWeb.setText(_translate('MainWindow', u'English Forum'))
self.actionGoGithub.setText(_translate('MainWindow', u'Github'))
'''
self.actionCnS.setText(_translate('MainWindow',u'Simplified Chinese'))
self.actionCnT.setText(_translate('MainWindow', u'Traditional Chinese'))
self.actionEn.setText(_translate('MainWindow',u'English'))
'''
self.actionCnS.setText(u'简体中文')
self.actionCnT.setText(u'繁體中文')
self.actionEn.setText(u'English')
self.actionLoadLanguage.setText(_translate('MainWindow',u'Load Language')) | python | def retranslateUi(self):
_translate = QtCore.QCoreApplication.translate
self.talk= _translate('MainWindow','You are using GeoPyTool ') + version +'\n'+ _translate('MainWindow','released on ') + date + '\n'
self.menuFile.setTitle(_translate('MainWindow', u'Data File'))
self.menuGeoChem.setTitle(_translate('MainWindow', u'Geochemistry'))
self.menuGeoCalc.setTitle(_translate('MainWindow',u'Calculation'))
self.menuStructure.setTitle(_translate('MainWindow', u'Structure'))
self.menuSedimentary.setTitle(_translate('MainWindow', u'Sedimentary'))
self.menuAdditional.setTitle(_translate('MainWindow', u'Additional Functions'))
self.menuHelp.setTitle(_translate('MainWindow', u'Help'))
self.menuLanguage.setTitle(_translate('MainWindow', u'Language'))
self.actionCombine.setText(_translate('MainWindow', u'Combine'))
self.actionCombine_transverse.setText(_translate('MainWindow', u'Combine_transverse'))
self.actionFlatten.setText(_translate('MainWindow',u'Flatten'))
self.actionTrans.setText(_translate('MainWindow',u'Trans'))
self.actionReFormat.setText(_translate('MainWindow',u'ReFormat'))
self.actionOpen.setText(_translate('MainWindow', u'Open Data'))
self.actionClose.setText(_translate('MainWindow', u'Close Data'))
self.actionSet.setText(_translate('MainWindow', u'Set Format'))
self.actionSave.setText(_translate('MainWindow', u'Save Data'))
self.actionQuit.setText(_translate('MainWindow', u'Quit App'))
self.actionRemoveLOI.setText('1-0 '+_translate('MainWindow',u'Remove LOI'))
self.actionAuto.setText('1-1 '+_translate('MainWindow', u'Auto'))
self.actionTAS.setText('1-2 '+ _translate('MainWindow',u'TAS'))
self.actionTrace.setText('1-3 '+_translate('MainWindow',u'Trace'))
self.actionRee.setText('1-4 '+_translate('MainWindow',u'REE'))
self.actionPearce.setText('1-5 '+_translate('MainWindow',u'Pearce'))
self.actionHarker.setText('1-6 '+_translate('MainWindow',u'Harker'))
self.actionCIPW.setText('1-7 '+_translate('MainWindow',u'CIPW'))
self.actionQAPF.setText('1-8 '+_translate('MainWindow',u'QAPF'))
self.actionSaccani.setText('1-9 '+_translate('MainWindow',u'Saccani Plot'))
self.actionK2OSiO2.setText('1-10 '+_translate('MainWindow',u'K2O-SiO2'))
self.actionRaman.setText('1-11 '+_translate('MainWindow',u'Raman Strength'))
self.actionFluidInclusion.setText('1-12 '+_translate('MainWindow',u'Fluid Inclusion'))
self.actionHarkerOld.setText('1-14 '+_translate('MainWindow',u'Harker Classical'))
self.actionTraceNew.setText('1-15 '+_translate('MainWindow',u'TraceNew'))
self.actionStereo.setText('2-1 '+_translate('MainWindow',u'Stereo'))
self.actionRose.setText('2-2 '+_translate('MainWindow',u'Rose'))
self.actionQFL.setText('3-1 '+_translate('MainWindow',u'QFL'))
self.actionQmFLt.setText('3-2 '+_translate('MainWindow',u'QmFLt'))
self.actionClastic.setText('3-3 '+_translate('MainWindow',u'Clastic'))
self.actionCIA.setText('3-4 '+ _translate('MainWindow',u'CIA and ICV'))
self.actionZirconCe.setText('4-1 '+ _translate('MainWindow',u'ZirconCe'))
self.actionZirconCeOld.setText('4-2 '+ _translate('MainWindow', u'ZirconCeOld'))
self.actionZirconTiTemp.setText('4-3 '+ _translate('MainWindow',u'ZirconTiTemp'))
self.actionRutileZrTemp.setText('4-4 '+_translate('MainWindow',u'RutileZrTemp'))
self.actionRbSrIsoTope.setText('4-5 '+_translate('MainWindow',u'Rb-Sr IsoTope'))
self.actionSmNdIsoTope.setText('4-6 '+_translate('MainWindow',u'Sm-Nd IsoTope'))
self.actionXY.setText('5-1 '+_translate('MainWindow',u'X-Y plot'))
self.actionXYZ.setText('5-2 '+_translate('MainWindow',u'X-Y-Z plot'))
self.actionCluster.setText('5-3 '+_translate('MainWindow',u'Cluster'))
self.actionMultiDimension.setText('5-4 '+_translate('MainWindow',u'MultiDimension'))
self.actionFA.setText('5-5 '+_translate('MainWindow',u'FA'))
self.actionPCA.setText('5-6 '+_translate('MainWindow',u'PCA'))
self.actionDist.setText('5-7 '+_translate('MainWindow',u'Distance'))
self.actionStatistics.setText('5-8 '+_translate('MainWindow',u'Statistics'))
self.actionThreeD.setText('5-9 '+_translate('MainWindow',u'ThreeD'))
self.actionTwoD.setText('5-10 '+_translate('MainWindow',u'TwoD'))
self.actionTwoD_Grey.setText('5-11 '+_translate('MainWindow',u'TwoD Grey'))
self.actionMyHist.setText('5-12 '+_translate('MainWindow',u'Histogram + KDE Curve'))
self.actionVersionCheck.setText(_translate('MainWindow', u'Check Update'))
self.actionWeb.setText(_translate('MainWindow', u'English Forum'))
self.actionGoGithub.setText(_translate('MainWindow', u'Github'))
self.actionCnS.setText(u'简体中文')
self.actionCnT.setText(u'繁體中文')
self.actionEn.setText(u'English')
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self.actionCnT.setText(_translate('MainWindow', u'Traditional Chinese'))
self.actionEn.setText(_translate('MainWindow',u'English')) | [
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| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/__init__.py#L580-L675 |
GeoPyTool/GeoPyTool | geopytool/Rose.py | Rose.singlerose | def singlerose(self, Width=1, Color=['red']):
'''
draw the rose map of single sample with different items~
'''
self.chooser_label.setText(self.ChooseItems[self.chooser.value() - 1])
self.MultipleRoseName = self.ChooseItems[self.chooser.value() - 1]
self.SingleRoseName = [(self.ChooseItems[self.chooser.value() - 1])]
Name = self.SingleRoseName
self.axes.clear()
# self.axes.set_xlim(-90, 450)
# self.axes.set_ylim(0, 90)
titles = list('NWSE')
titles = ['N', '330', '300', 'W', '240', '210', 'S', '150', '120', 'E', '60', '30']
self.n = len(titles)
self.angles = np.arange(90, 90 + 360, 360.0 / self.n)
self.angles = np.array([90., 120., 150., 180., 210., 240., 270., 300., 330.,
360., 30., 60.])
self.axes.set_thetagrids(self.angles, labels=titles, fontsize=14)
self.raw = self._df
real_max = []
for k in range(len(Name)):
Data = []
S = []
R = []
for i in range(len(self.raw)):
S.append(self.raw.at[i, Name[k]])
s = np.linspace(0, 360, 360 / self.Gap + 1)
t = tuple(s.tolist())
count = []
for i in range(len(t)):
tmp_count = 0
for j in S:
if i < len(t) - 1:
if t[i] < j <= t[i + 1]:
tmp_count += 1
count.append(tmp_count)
count_max = max(count)
real_max.append(count_max)
maxuse = max(real_max)
for k in range(len(Name)):
Data = []
S = []
R = []
for i in range(len(self.raw)):
S.append(self.raw.at[i, Name[k]])
s = np.linspace(0, 360, 360 / self.Gap + 1)
t = tuple(s.tolist())
count = []
for i in range(len(t)):
tmp_count = 0
for j in S:
if i < len(t) - 1:
if t[i] < j <= t[i + 1]:
tmp_count += 1
count.append(tmp_count)
s = np.linspace(0, 360, 360 / self.Gap + 1)
t = tuple(s.tolist())
R_factor = 90 / maxuse
for i in count:
TMP = 90 - i * R_factor
R.append(TMP)
m, n = self.Trans(t, R)
self.axes.plot(m, n, color=Color[k], linewidth=1, alpha=0.6, marker='')
self.axes.fill(m, n, Color=Color[k], Alpha=0.6, )
if (self.Type_cb.isChecked()):
self.Type_cb.setText('Wulf')
list1 = [self.eqan(x) for x in range(15, 90, 15)]
else:
self.Type_cb.setText('Schmidt')
list1 = [self.eqar(x) for x in range(15, 90, 15)]
list2= list1
print(maxuse + 1)
try:
list2 = [str(x) for x in range(0, int(maxuse + 1), int((maxuse + 1.0) / 7.0))]
except(ValueError):
pass
list2.reverse()
self.axes.set_rgrids(list1, list2)
#self.axes.set_thetagrids(range(360 + 90, 0 + 90, -15), [str(x) for x in range(0, 360, 15)])
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.5, 1), loc=2, borderaxespad=0, prop=fontprop) | python | def singlerose(self, Width=1, Color=['red']):
self.chooser_label.setText(self.ChooseItems[self.chooser.value() - 1])
self.MultipleRoseName = self.ChooseItems[self.chooser.value() - 1]
self.SingleRoseName = [(self.ChooseItems[self.chooser.value() - 1])]
Name = self.SingleRoseName
self.axes.clear()
titles = list('NWSE')
titles = ['N', '330', '300', 'W', '240', '210', 'S', '150', '120', 'E', '60', '30']
self.n = len(titles)
self.angles = np.arange(90, 90 + 360, 360.0 / self.n)
self.angles = np.array([90., 120., 150., 180., 210., 240., 270., 300., 330.,
360., 30., 60.])
self.axes.set_thetagrids(self.angles, labels=titles, fontsize=14)
self.raw = self._df
real_max = []
for k in range(len(Name)):
Data = []
S = []
R = []
for i in range(len(self.raw)):
S.append(self.raw.at[i, Name[k]])
s = np.linspace(0, 360, 360 / self.Gap + 1)
t = tuple(s.tolist())
count = []
for i in range(len(t)):
tmp_count = 0
for j in S:
if i < len(t) - 1:
if t[i] < j <= t[i + 1]:
tmp_count += 1
count.append(tmp_count)
count_max = max(count)
real_max.append(count_max)
maxuse = max(real_max)
for k in range(len(Name)):
Data = []
S = []
R = []
for i in range(len(self.raw)):
S.append(self.raw.at[i, Name[k]])
s = np.linspace(0, 360, 360 / self.Gap + 1)
t = tuple(s.tolist())
count = []
for i in range(len(t)):
tmp_count = 0
for j in S:
if i < len(t) - 1:
if t[i] < j <= t[i + 1]:
tmp_count += 1
count.append(tmp_count)
s = np.linspace(0, 360, 360 / self.Gap + 1)
t = tuple(s.tolist())
R_factor = 90 / maxuse
for i in count:
TMP = 90 - i * R_factor
R.append(TMP)
m, n = self.Trans(t, R)
self.axes.plot(m, n, color=Color[k], linewidth=1, alpha=0.6, marker='')
self.axes.fill(m, n, Color=Color[k], Alpha=0.6, )
if (self.Type_cb.isChecked()):
self.Type_cb.setText('Wulf')
list1 = [self.eqan(x) for x in range(15, 90, 15)]
else:
self.Type_cb.setText('Schmidt')
list1 = [self.eqar(x) for x in range(15, 90, 15)]
list2= list1
print(maxuse + 1)
try:
list2 = [str(x) for x in range(0, int(maxuse + 1), int((maxuse + 1.0) / 7.0))]
except(ValueError):
pass
list2.reverse()
self.axes.set_rgrids(list1, list2)
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.5, 1), loc=2, borderaxespad=0, prop=fontprop) | [
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| draw the rose map of single sample with different items~ | [
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"the",
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"sample",
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"items~"
]
| train | https://github.com/GeoPyTool/GeoPyTool/blob/8c198aa42e4fbdf62fac05d40cbf4d1086328da3/geopytool/Rose.py#L154-L265 |
GeoPyTool/GeoPyTool | geopytool/Rose.py | Rose.multirose | def multirose(self, Width=1, Name='Dip'):
'''
draw the rose map of multiple samples~
'''
Name = self.MultipleRoseName
self.axes.clear()
# self.axes.set_xlim(-90, 450)
# self.axes.set_ylim(0, 90)
titles = list('NWSE')
titles = ['N', '330', '300', 'W', '240', '210', 'S', '150', '120', 'E', '60', '30']
self.n = len(titles)
self.angles = np.arange(90, 90 + 360, 360.0 / self.n)
self.angles = np.array([90., 120., 150., 180., 210., 240., 270., 300., 330.,
360., 30., 60.])
self.axes.set_thetagrids(self.angles, labels=titles, fontsize=14)
self.raw = self._df
real_max = []
S = []
R = []
Color = []
Label = []
Whole = {}
for i in range(len(self.raw)):
S.append(self.raw.at[i, Name])
if self.raw.at[i, 'Color'] not in Color and self.raw.at[i, 'Color'] != '':
Color.append(self.raw.at[i, 'Color'])
if self.raw.at[i, 'Label'] not in Label and self.raw.at[i, 'Label'] != '':
Label.append(self.raw.at[i, 'Label'])
Whole = ({k: [] for k in Label})
WholeCount = ({k: [] for k in Label})
for i in range(len(self.raw)):
for k in Label:
if self.raw.at[i, 'Label'] == k:
Whole[k].append(self.raw.at[i, Name])
t = tuple(np.linspace(0, 360, 360 / self.Gap + 1).tolist())
real_max = 0
for j in range(len(Label)):
for i in range(len(t)):
tmp_count = 0
for u in Whole[Label[j]]:
if i < len(t) - 1:
if t[i] < u <= t[i + 1]:
tmp_count += 1
real_max = max(real_max, tmp_count)
WholeCount[Label[j]].append(tmp_count)
maxuse = real_max
R_factor = 90 / maxuse
for j in range(len(Label)):
R = []
for i in WholeCount[Label[j]]:
TMP = 90 - i * R_factor
R.append(TMP)
m, n = self.Trans(t, R)
self.axes.plot(m, n, color=Color[j], linewidth=1, alpha=0.6, marker='', label=Label[j])
self.axes.fill(m, n, Color=Color[j], Alpha=0.6)
if (self.Type_cb.isChecked()):
self.Type_cb.setText('Wulf')
list1 = [self.eqan(x) for x in range(15, 90, 15)]
else:
self.Type_cb.setText('Schmidt')
list1 = [self.eqar(x) for x in range(15, 90, 15)]
list2= list1
try:
list2 = [str(x) for x in range(0, int(maxuse + 1), int((maxuse + 1) / 7))]
except(ValueError):
pass
list2.reverse()
self.axes.set_rgrids(list1, list2)
#self.axes.set_thetagrids(range(360 + 90, 0 + 90, -15), [str(x) for x in range(0, 360, 15)])
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.5, 1), loc=2, borderaxespad=0, prop=fontprop) | python | def multirose(self, Width=1, Name='Dip'):
Name = self.MultipleRoseName
self.axes.clear()
titles = list('NWSE')
titles = ['N', '330', '300', 'W', '240', '210', 'S', '150', '120', 'E', '60', '30']
self.n = len(titles)
self.angles = np.arange(90, 90 + 360, 360.0 / self.n)
self.angles = np.array([90., 120., 150., 180., 210., 240., 270., 300., 330.,
360., 30., 60.])
self.axes.set_thetagrids(self.angles, labels=titles, fontsize=14)
self.raw = self._df
real_max = []
S = []
R = []
Color = []
Label = []
Whole = {}
for i in range(len(self.raw)):
S.append(self.raw.at[i, Name])
if self.raw.at[i, 'Color'] not in Color and self.raw.at[i, 'Color'] != '':
Color.append(self.raw.at[i, 'Color'])
if self.raw.at[i, 'Label'] not in Label and self.raw.at[i, 'Label'] != '':
Label.append(self.raw.at[i, 'Label'])
Whole = ({k: [] for k in Label})
WholeCount = ({k: [] for k in Label})
for i in range(len(self.raw)):
for k in Label:
if self.raw.at[i, 'Label'] == k:
Whole[k].append(self.raw.at[i, Name])
t = tuple(np.linspace(0, 360, 360 / self.Gap + 1).tolist())
real_max = 0
for j in range(len(Label)):
for i in range(len(t)):
tmp_count = 0
for u in Whole[Label[j]]:
if i < len(t) - 1:
if t[i] < u <= t[i + 1]:
tmp_count += 1
real_max = max(real_max, tmp_count)
WholeCount[Label[j]].append(tmp_count)
maxuse = real_max
R_factor = 90 / maxuse
for j in range(len(Label)):
R = []
for i in WholeCount[Label[j]]:
TMP = 90 - i * R_factor
R.append(TMP)
m, n = self.Trans(t, R)
self.axes.plot(m, n, color=Color[j], linewidth=1, alpha=0.6, marker='', label=Label[j])
self.axes.fill(m, n, Color=Color[j], Alpha=0.6)
if (self.Type_cb.isChecked()):
self.Type_cb.setText('Wulf')
list1 = [self.eqan(x) for x in range(15, 90, 15)]
else:
self.Type_cb.setText('Schmidt')
list1 = [self.eqar(x) for x in range(15, 90, 15)]
list2= list1
try:
list2 = [str(x) for x in range(0, int(maxuse + 1), int((maxuse + 1) / 7))]
except(ValueError):
pass
list2.reverse()
self.axes.set_rgrids(list1, list2)
if (self.legend_cb.isChecked()):
self.axes.legend(bbox_to_anchor=(1.5, 1), loc=2, borderaxespad=0, prop=fontprop) | [
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HDI-Project/MLPrimitives | mlprimitives/adapters/pandas.py | resample | def resample(df, rule, time_index, groupby=None, aggregation='mean'):
"""pd.DataFrame.resample adapter.
Call the `df.resample` method on the given time_index
and afterwards call the indicated aggregation.
Optionally group the dataframe by the indicated columns before
performing the resampling.
If groupby option is used, the result is a multi-index datagrame.
Args:
df (pandas.DataFrame): DataFrame to resample.
rule (str): The offset string or object representing target conversion.
groupby (list): Optional list of columns to group by.
time_index (str): Name of the column to use as the time index.
aggregation (str): Name of the aggregation function to use.
Returns:
pandas.Dataframe: resampled dataframe
"""
if groupby:
df = df.groupby(groupby)
df = df.resample(rule, on=time_index)
df = getattr(df, aggregation)()
for column in groupby:
del df[column]
return df | python | def resample(df, rule, time_index, groupby=None, aggregation='mean'):
if groupby:
df = df.groupby(groupby)
df = df.resample(rule, on=time_index)
df = getattr(df, aggregation)()
for column in groupby:
del df[column]
return df | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/adapters/pandas.py#L1-L30 |
HDI-Project/MLPrimitives | mlprimitives/adapters/pandas.py | _join_names | def _join_names(names):
"""Join the names of a multi-level index with an underscore."""
levels = (str(name) for name in names if name != '')
return '_'.join(levels) | python | def _join_names(names):
levels = (str(name) for name in names if name != '')
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/adapters/pandas.py#L33-L37 |
HDI-Project/MLPrimitives | mlprimitives/adapters/pandas.py | unstack | def unstack(df, level=-1, reset_index=True):
"""pd.DataFrame.unstack adapter.
Call the `df.unstack` method using the indicated level and afterwards
join the column names using an underscore.
Args:
df (pandas.DataFrame): DataFrame to unstack.
level (str, int or list): Level(s) of index to unstack, can pass level name
reset_index (bool): Whether to reset the index after unstacking
Returns:
pandas.Dataframe: unstacked dataframe
"""
df = df.unstack(level=level)
if reset_index:
df = df.reset_index()
df.columns = df.columns.map(_join_names)
return df | python | def unstack(df, level=-1, reset_index=True):
df = df.unstack(level=level)
if reset_index:
df = df.reset_index()
df.columns = df.columns.map(_join_names)
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/adapters/pandas.py#L40-L59 |
HDI-Project/MLPrimitives | mlprimitives/candidates/dsp.py | next_power_of_2 | def next_power_of_2(x):
"""Finds the next power of 2 value
Args:
x: Input value
Returns:
power_of_2: Next power of 2 value
"""
power_of_2 = 1 if x == 0 else 2 ** np.ceil(np.log2(x))
return power_of_2 | python | def next_power_of_2(x):
power_of_2 = 1 if x == 0 else 2 ** np.ceil(np.log2(x))
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/dsp.py#L4-L16 |
HDI-Project/MLPrimitives | mlprimitives/candidates/dsp.py | SpectralMask.window_design | def window_design(self, window_length, beta):
"""Kaiser window design
Args:
window_length: Length of the window in number of samples
beta: Beta value for Kaiser window design
Returns:
window: Window designed using the beta and length provided as inputs
"""
self.window = np.kaiser(window_length, beta)
return self.window | python | def window_design(self, window_length, beta):
self.window = np.kaiser(window_length, beta)
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/dsp.py#L44-L58 |
HDI-Project/MLPrimitives | mlprimitives/candidates/dsp.py | SpectralMask.fit | def fit(self, X):
"""Defines a spectral mask based on training data
Args:
X: Training data
"""
training_signal = X
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training_signal = X
self.window_design(self.window_length, self.beta)
if self.method == 'std_dev':
self.fit_freq_std_dev(training_signal)
elif self.method == 'min_max':
self.fit_freq_min_max(training_signal)
else:
raise ValueError('Unknown method: {}'.format(self.method)) | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/dsp.py#L60-L77 |
HDI-Project/MLPrimitives | mlprimitives/candidates/dsp.py | SpectralMask.fit_freq_min_max | def fit_freq_min_max(self, training_signal):
"""Defines a spectral mask based on training data using min and max values of each
frequency component
Args:
training_signal: Training data
"""
window_length = len(self.window)
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max_mask = np.zeros(int(window_length / 2) + 1)
min_mask = np.zeros(int(window_length / 2) + 1)
for i in range(0, len(training_signal) - window_length - 1):
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self.mask_bottom = min_mask / self.gain | python | def fit_freq_min_max(self, training_signal):
window_length = len(self.window)
window_weight = sum(self.window)
max_mask = np.zeros(int(window_length / 2) + 1)
min_mask = np.zeros(int(window_length / 2) + 1)
for i in range(0, len(training_signal) - window_length - 1):
rfft = np.fft.rfft(training_signal[i:i + window_length] * self.window)
temp = np.abs(rfft) / window_weight
max_mask = np.maximum(max_mask, temp)
min_mask = np.minimum(min_mask, temp)
self.mask_top = self.gain * max_mask
self.mask_bottom = min_mask / self.gain | [
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HDI-Project/MLPrimitives | mlprimitives/candidates/dsp.py | SpectralMask.fit_freq_std_dev | def fit_freq_std_dev(self, training_signal):
"""Defines a spectral mask based on training data using the standard deviation values of
each frequency component
Args:
training_signal: Training data
"""
window_length = len(self.window)
window_weight = sum(self.window)
num_of_windows = len(training_signal) - window_length - 1
mean = np.zeros(int(window_length / 2) + 1)
pow = np.zeros(int(window_length / 2) + 1)
temp = np.zeros(int(window_length / 2) + 1)
rfft = np.fft.rfft(training_signal[0:0 + window_length] * self.window)
max = np.abs(rfft) / window_weight
min = max
for i in range(0, num_of_windows):
rfft = np.fft.rfft(training_signal[i:i + window_length] * self.window)
temp = np.abs(rfft) / window_weight
max = np.maximum(temp, max)
min = np.minimum(temp, min)
mean = mean + temp
pow = pow + np.power(temp, 2)
mean = mean / num_of_windows
pow = pow / num_of_windows
std_dev = np.sqrt(pow - np.power(mean, 2))
self.mask_top = mean + self.gain * std_dev
self.mask_bottom = np.maximum(mean - self.gain * std_dev,
np.zeros(int(window_length / 2) + 1)) | python | def fit_freq_std_dev(self, training_signal):
window_length = len(self.window)
window_weight = sum(self.window)
num_of_windows = len(training_signal) - window_length - 1
mean = np.zeros(int(window_length / 2) + 1)
pow = np.zeros(int(window_length / 2) + 1)
temp = np.zeros(int(window_length / 2) + 1)
rfft = np.fft.rfft(training_signal[0:0 + window_length] * self.window)
max = np.abs(rfft) / window_weight
min = max
for i in range(0, num_of_windows):
rfft = np.fft.rfft(training_signal[i:i + window_length] * self.window)
temp = np.abs(rfft) / window_weight
max = np.maximum(temp, max)
min = np.minimum(temp, min)
mean = mean + temp
pow = pow + np.power(temp, 2)
mean = mean / num_of_windows
pow = pow / num_of_windows
std_dev = np.sqrt(pow - np.power(mean, 2))
self.mask_top = mean + self.gain * std_dev
self.mask_bottom = np.maximum(mean - self.gain * std_dev,
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HDI-Project/MLPrimitives | mlprimitives/candidates/dsp.py | SpectralMask.produce | def produce(self, X):
"""Detects anomalies in telemetry data based on its power spectral density
Args:
X: Telemetry data
Returns:
anomalies: Data vector consisting of the anomalies detected in the telemetry data
"""
signal = X
window_length = len(self.window)
anomalies = np.zeros(len(signal))
window_weight = sum(self.window)
for i in range(0, len(signal) - window_length - 1):
rfft = np.fft.rfft(signal[i:i + window_length] * self.window)
sig_freq = np.abs(rfft) / window_weight
anomalies[i] = 0
for m in range(0, int(window_length / 2) - 1):
if ((sig_freq[m] > self.mask_top[m]) or (sig_freq[m] < self.mask_bottom[m])):
anomalies[i] = 1
break
return anomalies | python | def produce(self, X):
signal = X
window_length = len(self.window)
anomalies = np.zeros(len(signal))
window_weight = sum(self.window)
for i in range(0, len(signal) - window_length - 1):
rfft = np.fft.rfft(signal[i:i + window_length] * self.window)
sig_freq = np.abs(rfft) / window_weight
anomalies[i] = 0
for m in range(0, int(window_length / 2) - 1):
if ((sig_freq[m] > self.mask_top[m]) or (sig_freq[m] < self.mask_bottom[m])):
anomalies[i] = 1
break
return anomalies | [
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HDI-Project/MLPrimitives | mlprimitives/datasets.py | load_boston_multitask | def load_boston_multitask():
"""Boston House Prices Dataset with a synthetic multitask output.
The multitask output is obtained by applying a linear transformation
to the original y and adding it as a second output column.
"""
dataset = datasets.load_boston()
y = dataset.target
target = np.column_stack([y, 2 * y + 5])
return Dataset(load_boston.__doc__, dataset.data, target, r2_score) | python | def load_boston_multitask():
dataset = datasets.load_boston()
y = dataset.target
target = np.column_stack([y, 2 * y + 5])
return Dataset(load_boston.__doc__, dataset.data, target, r2_score) | [
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HDI-Project/MLPrimitives | mlprimitives/candidates/audio_featurization.py | energy | def energy(data):
"""Computes signal energy of data"""
data = np.mean(data, axis=1)
return np.sum(data ** 2) / np.float64(len(data)) | python | def energy(data):
data = np.mean(data, axis=1)
return np.sum(data ** 2) / np.float64(len(data)) | [
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HDI-Project/MLPrimitives | mlprimitives/candidates/audio_featurization.py | spectral_entropy | def spectral_entropy(data, numOfShortBlocks=10):
"""Computes the spectral entropy"""
data = np.mean(data, axis=1)
nFFT = len(data) // 2
X = FFT(data, nFFT)
L = len(X) # number of frame data
Eol = np.sum(X ** 2) # total spectral energy
subWinLength = int(np.floor(L / numOfShortBlocks)) # length of sub-frame
if L != subWinLength * numOfShortBlocks:
X = X[0:subWinLength * numOfShortBlocks]
# define sub-frames (using matrix reshape)
subWindows = X.reshape(subWinLength, numOfShortBlocks, order='F').copy()
# compute spectral sub-energies
s = np.sum(subWindows ** 2, axis=0) / (Eol + EPSILON)
# compute spectral entropy
return -np.sum(s * np.log2(s + EPSILON)) | python | def spectral_entropy(data, numOfShortBlocks=10):
data = np.mean(data, axis=1)
nFFT = len(data) // 2
X = FFT(data, nFFT)
L = len(X)
Eol = np.sum(X ** 2)
subWinLength = int(np.floor(L / numOfShortBlocks))
if L != subWinLength * numOfShortBlocks:
X = X[0:subWinLength * numOfShortBlocks]
subWindows = X.reshape(subWinLength, numOfShortBlocks, order='F').copy()
s = np.sum(subWindows ** 2, axis=0) / (Eol + EPSILON)
return -np.sum(s * np.log2(s + EPSILON)) | [
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HDI-Project/MLPrimitives | mlprimitives/candidates/audio_featurization.py | zcr | def zcr(data):
"""Computes zero crossing rate of segment"""
data = np.mean(data, axis=1)
count = len(data)
countZ = np.sum(np.abs(np.diff(np.sign(data)))) / 2
return (np.float64(countZ) / np.float64(count - 1.0)) | python | def zcr(data):
data = np.mean(data, axis=1)
count = len(data)
countZ = np.sum(np.abs(np.diff(np.sign(data)))) / 2
return (np.float64(countZ) / np.float64(count - 1.0)) | [
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HDI-Project/MLPrimitives | mlprimitives/candidates/audio_featurization.py | spectral_flux | def spectral_flux(d0, d1):
"""
Computes the spectral flux feature of the current frame
"""
# compute the spectral flux as the sum of square distances:
d0 = np.mean(d0, axis=1)
d1 = np.mean(d1, axis=1)
nFFT = min(len(d0) // 2, len(d1) // 2)
X = FFT(d0, nFFT)
Xprev = FFT(d1, nFFT)
# L = min(len(X), len(Xprev))
sumX = np.sum(X + EPSILON)
sumPrevX = np.sum(Xprev + EPSILON)
return np.sum((X / sumX - Xprev / sumPrevX) ** 2) | python | def spectral_flux(d0, d1):
d0 = np.mean(d0, axis=1)
d1 = np.mean(d1, axis=1)
nFFT = min(len(d0) // 2, len(d1) // 2)
X = FFT(d0, nFFT)
Xprev = FFT(d1, nFFT)
sumX = np.sum(X + EPSILON)
sumPrevX = np.sum(Xprev + EPSILON)
return np.sum((X / sumX - Xprev / sumPrevX) ** 2) | [
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HDI-Project/MLPrimitives | mlprimitives/candidates/audio_featurization.py | energy_entropy | def energy_entropy(data, fs, numOfShortBlocks=10):
"""Computes entropy of energy"""
data = np.mean(data, axis=1)
Eol = np.sum(data ** 2) # total data energy
L = len(data)
subWinLength = int(np.floor(L / numOfShortBlocks))
if L != subWinLength * numOfShortBlocks:
data = data[0:subWinLength * numOfShortBlocks]
# subWindows is of size [numOfShortBlocks x L]
subWindows = data.reshape(subWinLength, numOfShortBlocks, order='F').copy()
# Compute normalized sub-data energies:
s = np.sum(subWindows ** 2, axis=0) / (Eol + EPSILON)
# Compute entropy of the normalized sub-data energies:
Entropy = -np.sum(s * np.log2(s + EPSILON))
return Entropy | python | def energy_entropy(data, fs, numOfShortBlocks=10):
data = np.mean(data, axis=1)
Eol = np.sum(data ** 2)
L = len(data)
subWinLength = int(np.floor(L / numOfShortBlocks))
if L != subWinLength * numOfShortBlocks:
data = data[0:subWinLength * numOfShortBlocks]
subWindows = data.reshape(subWinLength, numOfShortBlocks, order='F').copy()
s = np.sum(subWindows ** 2, axis=0) / (Eol + EPSILON)
Entropy = -np.sum(s * np.log2(s + EPSILON))
return Entropy | [
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HDI-Project/MLPrimitives | mlprimitives/candidates/audio_featurization.py | spectral_centroid_and_spread | def spectral_centroid_and_spread(data, fs):
"""Computes spectral centroid of frame (given abs(FFT))"""
data = np.mean(data, axis=1)
nFFT = len(data) // 2
X = FFT(data, nFFT)
ind = (np.arange(1, len(X) + 1)) * (fs / (2.0 * len(X)))
Xt = X.copy()
Xt = Xt / Xt.max()
NUM = np.sum(ind * Xt)
DEN = np.sum(Xt) + EPSILON
# Centroid:
C = (NUM / DEN)
# Spread:
S = np.sqrt(np.sum(((ind - C) ** 2) * Xt) / DEN)
# Normalize:
C = C / (fs / 2.0)
S = S / (fs / 2.0)
return (C, S) | python | def spectral_centroid_and_spread(data, fs):
data = np.mean(data, axis=1)
nFFT = len(data) // 2
X = FFT(data, nFFT)
ind = (np.arange(1, len(X) + 1)) * (fs / (2.0 * len(X)))
Xt = X.copy()
Xt = Xt / Xt.max()
NUM = np.sum(ind * Xt)
DEN = np.sum(Xt) + EPSILON
C = (NUM / DEN)
S = np.sqrt(np.sum(((ind - C) ** 2) * Xt) / DEN)
C = C / (fs / 2.0)
S = S / (fs / 2.0)
return (C, S) | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/audio_featurization.py#L99-L123 |
HDI-Project/MLPrimitives | mlprimitives/candidates/audio_featurization.py | spectral_rolloff | def spectral_rolloff(data, coeff):
"""Computes spectral roll-off"""
data = np.mean(data, axis=1)
nFFT = len(data) // 2
X = FFT(data, nFFT)
totalEnergy = np.sum(X ** 2)
fftLength = len(X)
Thres = coeff * totalEnergy
# Find the spectral rolloff as the frequency position where the
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CumSum = np.cumsum(X ** 2) + EPSILON
[a, ] = np.nonzero(CumSum > Thres)
if len(a) > 0:
mC = np.float64(a[0]) / (float(fftLength))
else:
mC = 0.0
return mC | python | def spectral_rolloff(data, coeff):
data = np.mean(data, axis=1)
nFFT = len(data) // 2
X = FFT(data, nFFT)
totalEnergy = np.sum(X ** 2)
fftLength = len(X)
Thres = coeff * totalEnergy
CumSum = np.cumsum(X ** 2) + EPSILON
[a, ] = np.nonzero(CumSum > Thres)
if len(a) > 0:
mC = np.float64(a[0]) / (float(fftLength))
else:
mC = 0.0
return mC | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/audio_featurization.py#L126-L145 |
HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_preprocessing.py | rolling_window_sequences | def rolling_window_sequences(X, index, window_size, target_size, target_column):
"""Create rolling window sequences out of timeseries data."""
out_X = list()
out_y = list()
X_index = list()
y_index = list()
target = X[:, target_column]
for start in range(len(X) - window_size - target_size + 1):
end = start + window_size
out_X.append(X[start:end])
out_y.append(target[end:end + target_size])
X_index.append(index[start])
y_index.append(index[end])
return np.asarray(out_X), np.asarray(out_y), np.asarray(X_index), np.asarray(y_index) | python | def rolling_window_sequences(X, index, window_size, target_size, target_column):
out_X = list()
out_y = list()
X_index = list()
y_index = list()
target = X[:, target_column]
for start in range(len(X) - window_size - target_size + 1):
end = start + window_size
out_X.append(X[start:end])
out_y.append(target[end:end + target_size])
X_index.append(index[start])
y_index.append(index[end])
return np.asarray(out_X), np.asarray(out_y), np.asarray(X_index), np.asarray(y_index) | [
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HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_preprocessing.py | time_segments_average | def time_segments_average(X, interval, time_column):
"""Compute average of values over fixed length time segments."""
warnings.warn(_TIME_SEGMENTS_AVERAGE_DEPRECATION_WARNING, DeprecationWarning)
if isinstance(X, np.ndarray):
X = pd.DataFrame(X)
X = X.sort_values(time_column).set_index(time_column)
start_ts = X.index.values[0]
max_ts = X.index.values[-1]
values = list()
index = list()
while start_ts <= max_ts:
end_ts = start_ts + interval
subset = X.loc[start_ts:end_ts - 1]
means = subset.mean(skipna=True).values
values.append(means)
index.append(start_ts)
start_ts = end_ts
return np.asarray(values), np.asarray(index) | python | def time_segments_average(X, interval, time_column):
warnings.warn(_TIME_SEGMENTS_AVERAGE_DEPRECATION_WARNING, DeprecationWarning)
if isinstance(X, np.ndarray):
X = pd.DataFrame(X)
X = X.sort_values(time_column).set_index(time_column)
start_ts = X.index.values[0]
max_ts = X.index.values[-1]
values = list()
index = list()
while start_ts <= max_ts:
end_ts = start_ts + interval
subset = X.loc[start_ts:end_ts - 1]
means = subset.mean(skipna=True).values
values.append(means)
index.append(start_ts)
start_ts = end_ts
return np.asarray(values), np.asarray(index) | [
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HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_preprocessing.py | time_segments_aggregate | def time_segments_aggregate(X, interval, time_column, method=['mean']):
"""Aggregate values over fixed length time segments."""
if isinstance(X, np.ndarray):
X = pd.DataFrame(X)
X = X.sort_values(time_column).set_index(time_column)
if isinstance(method, str):
method = [method]
start_ts = X.index.values[0]
max_ts = X.index.values[-1]
values = list()
index = list()
while start_ts <= max_ts:
end_ts = start_ts + interval
subset = X.loc[start_ts:end_ts - 1]
aggregated = [
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values.append(np.concatenate(aggregated))
index.append(start_ts)
start_ts = end_ts
return np.asarray(values), np.asarray(index) | python | def time_segments_aggregate(X, interval, time_column, method=['mean']):
if isinstance(X, np.ndarray):
X = pd.DataFrame(X)
X = X.sort_values(time_column).set_index(time_column)
if isinstance(method, str):
method = [method]
start_ts = X.index.values[0]
max_ts = X.index.values[-1]
values = list()
index = list()
while start_ts <= max_ts:
end_ts = start_ts + interval
subset = X.loc[start_ts:end_ts - 1]
aggregated = [
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values.append(np.concatenate(aggregated))
index.append(start_ts)
start_ts = end_ts
return np.asarray(values), np.asarray(index) | [
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HDI-Project/MLPrimitives | mlprimitives/utils.py | image_transform | def image_transform(X, function, reshape_before=False, reshape_after=False,
width=None, height=None, **kwargs):
"""Apply a function image by image.
Args:
reshape_before: whether 1d array needs to be reshaped to a 2d image
reshape_after: whether the returned values need to be reshaped back to a 1d array
width: image width used to rebuild the 2d images. Required if the image is not square.
height: image height used to rebuild the 2d images. Required if the image is not square.
"""
if not callable(function):
function = import_object(function)
elif not callable(function):
raise ValueError("function must be a str or a callable")
flat_image = len(X[0].shape) == 1
if reshape_before and flat_image:
if not (width and height):
side_length = math.sqrt(X.shape[1])
if side_length.is_integer():
side_length = int(side_length)
width = side_length
height = side_length
else:
raise ValueError("Image sizes must be given for non-square images")
else:
reshape_before = False
new_X = []
for image in X:
if reshape_before:
image = image.reshape((width, height))
features = function(
image,
**kwargs
)
if reshape_after:
features = np.reshape(features, X.shape[1])
new_X.append(features)
return np.array(new_X) | python | def image_transform(X, function, reshape_before=False, reshape_after=False,
width=None, height=None, **kwargs):
if not callable(function):
function = import_object(function)
elif not callable(function):
raise ValueError("function must be a str or a callable")
flat_image = len(X[0].shape) == 1
if reshape_before and flat_image:
if not (width and height):
side_length = math.sqrt(X.shape[1])
if side_length.is_integer():
side_length = int(side_length)
width = side_length
height = side_length
else:
raise ValueError("Image sizes must be given for non-square images")
else:
reshape_before = False
new_X = []
for image in X:
if reshape_before:
image = image.reshape((width, height))
features = function(
image,
**kwargs
)
if reshape_after:
features = np.reshape(features, X.shape[1])
new_X.append(features)
return np.array(new_X) | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/utils.py#L18-L65 |
HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_anomalies.py | regression_errors | def regression_errors(y, y_hat, smoothing_window=0.01, smooth=True):
"""Compute an array of absolute errors comparing predictions and expected output.
If smooth is True, apply EWMA to the resulting array of errors.
Args:
y (array): Ground truth.
y_hat (array): Predictions array.
smoothing_window (float): Size of the smoothing window, expressed as a proportion
of the total length of y.
smooth (bool): whether the returned errors should be smoothed with EWMA.
Returns:
(array): errors
"""
errors = np.abs(y - y_hat)[:, 0]
if not smooth:
return errors
smoothing_window = int(smoothing_window * len(y))
return pd.Series(errors).ewm(span=smoothing_window).mean().values | python | def regression_errors(y, y_hat, smoothing_window=0.01, smooth=True):
errors = np.abs(y - y_hat)[:, 0]
if not smooth:
return errors
smoothing_window = int(smoothing_window * len(y))
return pd.Series(errors).ewm(span=smoothing_window).mean().values | [
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HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_anomalies.py | deltas | def deltas(errors, epsilon, mean, std):
"""Compute mean and std deltas.
delta_mean = mean(errors) - mean(all errors below epsilon)
delta_std = std(errors) - std(all errors below epsilon)
"""
below = errors[errors <= epsilon]
if not len(below):
return 0, 0
return mean - below.mean(), std - below.std() | python | def deltas(errors, epsilon, mean, std):
below = errors[errors <= epsilon]
if not len(below):
return 0, 0
return mean - below.mean(), std - below.std() | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/custom/timeseries_anomalies.py#L36-L46 |
HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_anomalies.py | count_above | def count_above(errors, epsilon):
"""Count number of errors and continuous sequences above epsilon.
Continuous sequences are counted by shifting and counting the number
of positions where there was a change and the original value was true,
which means that a sequence started at that position.
"""
above = errors > epsilon
total_above = len(errors[above])
above = pd.Series(above)
shift = above.shift(1)
change = above != shift
total_consecutive = sum(above & change)
return total_above, total_consecutive | python | def count_above(errors, epsilon):
above = errors > epsilon
total_above = len(errors[above])
above = pd.Series(above)
shift = above.shift(1)
change = above != shift
total_consecutive = sum(above & change)
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/custom/timeseries_anomalies.py#L49-L65 |
HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_anomalies.py | z_cost | def z_cost(z, errors, mean, std):
"""Compute how bad a z value is.
The original formula is::
(delta_mean/mean) + (delta_std/std)
------------------------------------------------------
number of errors above + (number of sequences above)^2
which computes the "goodness" of `z`, meaning that the higher the value
the better the `z`.
In this case, we return this value inverted (we make it negative), to convert
it into a cost function, as later on we will use scipy to minimize it.
"""
epsilon = mean + z * std
delta_mean, delta_std = deltas(errors, epsilon, mean, std)
above, consecutive = count_above(errors, epsilon)
numerator = -(delta_mean / mean + delta_std / std)
denominator = above + consecutive ** 2
if denominator == 0:
return np.inf
return numerator / denominator | python | def z_cost(z, errors, mean, std):
epsilon = mean + z * std
delta_mean, delta_std = deltas(errors, epsilon, mean, std)
above, consecutive = count_above(errors, epsilon)
numerator = -(delta_mean / mean + delta_std / std)
denominator = above + consecutive ** 2
if denominator == 0:
return np.inf
return numerator / denominator | [
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HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_anomalies.py | find_threshold | def find_threshold(errors, z_range=(0, 10)):
"""Find the ideal threshold.
The ideal threshold is the one that minimizes the z_cost function.
"""
mean = errors.mean()
std = errors.std()
min_z, max_z = z_range
best_z = min_z
best_cost = np.inf
for z in range(min_z, max_z):
best = fmin(z_cost, z, args=(errors, mean, std), full_output=True, disp=False)
z, cost = best[0:2]
if cost < best_cost:
best_z = z[0]
return mean + best_z * std | python | def find_threshold(errors, z_range=(0, 10)):
mean = errors.mean()
std = errors.std()
min_z, max_z = z_range
best_z = min_z
best_cost = np.inf
for z in range(min_z, max_z):
best = fmin(z_cost, z, args=(errors, mean, std), full_output=True, disp=False)
z, cost = best[0:2]
if cost < best_cost:
best_z = z[0]
return mean + best_z * std | [
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HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_anomalies.py | find_sequences | def find_sequences(errors, epsilon):
"""Find sequences of values that are above epsilon.
This is done following this steps:
* create a boolean mask that indicates which value are above epsilon.
* shift this mask by one place, filing the empty gap with a False
* compare the shifted mask with the original one to see if there are changes.
* Consider a sequence start any point which was true and has changed
* Consider a sequence end any point which was false and has changed
"""
above = pd.Series(errors > epsilon)
shift = above.shift(1).fillna(False)
change = above != shift
index = above.index
starts = index[above & change].tolist()
ends = (index[~above & change] - 1).tolist()
if len(ends) == len(starts) - 1:
ends.append(len(above) - 1)
return list(zip(starts, ends)) | python | def find_sequences(errors, epsilon):
above = pd.Series(errors > epsilon)
shift = above.shift(1).fillna(False)
change = above != shift
index = above.index
starts = index[above & change].tolist()
ends = (index[~above & change] - 1).tolist()
if len(ends) == len(starts) - 1:
ends.append(len(above) - 1)
return list(zip(starts, ends)) | [
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HDI-Project/MLPrimitives | mlprimitives/custom/timeseries_anomalies.py | find_anomalies | def find_anomalies(errors, index, z_range=(0, 10)):
"""Find sequences of values that are anomalous.
We first find the ideal threshold for the set of errors that we have,
and then find the sequences of values that are above this threshold.
Lastly, we compute a score proportional to the maximum error in the
sequence, and finally return the index pairs that correspond to
each sequence, along with its score.
"""
threshold = find_threshold(errors, z_range)
sequences = find_sequences(errors, threshold)
anomalies = list()
denominator = errors.mean() + errors.std()
for start, stop in sequences:
max_error = errors[start:stop + 1].max()
score = (max_error - threshold) / denominator
anomalies.append([index[start], index[stop], score])
return np.asarray(anomalies) | python | def find_anomalies(errors, index, z_range=(0, 10)):
threshold = find_threshold(errors, z_range)
sequences = find_sequences(errors, threshold)
anomalies = list()
denominator = errors.mean() + errors.std()
for start, stop in sequences:
max_error = errors[start:stop + 1].max()
score = (max_error - threshold) / denominator
anomalies.append([index[start], index[stop], score])
return np.asarray(anomalies) | [
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Lastly, we compute a score proportional to the maximum error in the
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/custom/timeseries_anomalies.py#L143-L164 |
HDI-Project/MLPrimitives | mlprimitives/adapters/cv2.py | GaussianBlur | def GaussianBlur(X, ksize_width, ksize_height, sigma_x, sigma_y):
"""Apply Gaussian blur to the given data.
Args:
X: data to blur
kernel_size: Gaussian kernel size
stddev: Gaussian kernel standard deviation (in both X and Y directions)
"""
return image_transform(
X,
cv2.GaussianBlur,
ksize=(ksize_width, ksize_height),
sigmaX=sigma_x,
sigmaY=sigma_y
) | python | def GaussianBlur(X, ksize_width, ksize_height, sigma_x, sigma_y):
return image_transform(
X,
cv2.GaussianBlur,
ksize=(ksize_width, ksize_height),
sigmaX=sigma_x,
sigmaY=sigma_y
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/adapters/cv2.py#L8-L22 |
HDI-Project/MLPrimitives | mlprimitives/candidates/timeseries.py | rolling_window_sequences | def rolling_window_sequences(X, window_size, target_size, value_column, time_column):
"""
Function that takes in a pandas.DataFrame and a window_size then creates
output arrays that correspond to a timeseries sequence with window_size overlap.
The output arrays can be fed into a timeseries forecasting model.
Assumes the input is timeseries sorted.
Args:
X (pandas.DataFrame): a pandas dataframe which has 'timestamp'
and 'value' columns, and is sorted based on timestamp.
The timestamp column is in UNIX format (in seconds).
window_size (int): number of values that overlap to create the sequence.
value_column (string): name of column that has the value field.
time_column (string): name of column that has the time field.
Returns:
(numpy.ndarray): contains the time series sequenced data with each
entry having window_size rows.
(numpy.ndarray): acts as the label for the forecasting problem with
each entry having window_size rows.
(numpy.ndarray): the corresponding timestamps series.
"""
output_X = []
y = []
time = []
for start in range(len(X) - window_size - target_size):
end = start + window_size
output_X.append(X.iloc[start:end][value_column].values.reshape([-1, 1]))
y.append(X.iloc[end:end + target_size][value_column].values)
time.append(X.iloc[end + 1][time_column])
return np.asarray(output_X), np.asarray(y), np.asarray(time) | python | def rolling_window_sequences(X, window_size, target_size, value_column, time_column):
output_X = []
y = []
time = []
for start in range(len(X) - window_size - target_size):
end = start + window_size
output_X.append(X.iloc[start:end][value_column].values.reshape([-1, 1]))
y.append(X.iloc[end:end + target_size][value_column].values)
time.append(X.iloc[end + 1][time_column])
return np.asarray(output_X), np.asarray(y), np.asarray(time) | [
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HDI-Project/MLPrimitives | mlprimitives/candidates/timeseries.py | time_segments_average | def time_segments_average(X, interval, value_column, time_column):
"""
function that aggregates data in a pandas dataframe by averaging over a given interval.
it starts averaging from the smallest timestamp in the dataframe and ends at the
largest timestamp. assumes the input is timeseries sorted.
args:
X (pandas.dataframe): a pandas dataframe which has 'timestamp'
and 'value' columns, and is sorted based on timestamp. the timestamp
column is in unix format (in seconds).
interval (int): an integer denoting the number of seconds
in the desired interval.
value_column (string): name of column that has the value field.
time_column (string): name of column that has the time field.
returns:
pandas.dataframe: a pandas dataframe with two colums
('timestamp' and 'value'), where each `timestamp` is the starting time of
an interval and the `value` is the result of aggregation.
"""
start_ts = X[time_column].iloc[0] # min value
end_time = X[time_column].iloc[-1] # max value in dataframe
accepted_points = []
while start_ts < end_time:
# average the values between start_ts, [start_ts + timedelta (e.g. 6hrs)]
upper_ts = start_ts + interval
mask = (X[time_column] > start_ts) & (X[time_column] <= upper_ts)
average_value = X.loc[mask][value_column].mean(skipna=True)
accepted_points.append([start_ts, average_value])
start_ts = upper_ts # update the timestamp
return pd.DataFrame(accepted_points, columns=[time_column, value_column]) | python | def time_segments_average(X, interval, value_column, time_column):
start_ts = X[time_column].iloc[0]
end_time = X[time_column].iloc[-1]
accepted_points = []
while start_ts < end_time:
upper_ts = start_ts + interval
mask = (X[time_column] > start_ts) & (X[time_column] <= upper_ts)
average_value = X.loc[mask][value_column].mean(skipna=True)
accepted_points.append([start_ts, average_value])
start_ts = upper_ts
return pd.DataFrame(accepted_points, columns=[time_column, value_column]) | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/timeseries.py#L37-L67 |
HDI-Project/MLPrimitives | mlprimitives/candidates/timeseries_errors.py | get_forecast_errors | def get_forecast_errors(y_hat,
y_true,
window_size=5,
batch_size=30,
smoothing_percent=0.05,
smoothed=True):
"""
Calculates the forecasting error for two arrays of data. If smoothed errors desired,
runs EWMA.
Args:
y_hat (list): forecasted values. len(y_hat)==len(y_true).
y_true (list): true values. len(y_hat)==len(y_true).
window_size (int):
batch_size (int):
smoothing_percent (float):
smoothed (bool): whether the returned errors should be smoothed with EWMA.
Returns:
(list): error residuals. Smoothed if specified by user.
"""
errors = [abs(y_h - y_t) for y_h, y_t in zip(y_hat, y_true)]
if not smoothed:
return errors
historical_error_window = int(window_size * batch_size * smoothing_percent)
moving_avg = []
for i in range(len(errors)):
left_window = i - historical_error_window
right_window = i + historical_error_window + 1
if left_window < 0:
left_window = 0
if right_window > len(errors):
right_window = len(errors)
moving_avg.append(np.mean(errors[left_window:right_window]))
return moving_avg | python | def get_forecast_errors(y_hat,
y_true,
window_size=5,
batch_size=30,
smoothing_percent=0.05,
smoothed=True):
errors = [abs(y_h - y_t) for y_h, y_t in zip(y_hat, y_true)]
if not smoothed:
return errors
historical_error_window = int(window_size * batch_size * smoothing_percent)
moving_avg = []
for i in range(len(errors)):
left_window = i - historical_error_window
right_window = i + historical_error_window + 1
if left_window < 0:
left_window = 0
if right_window > len(errors):
right_window = len(errors)
moving_avg.append(np.mean(errors[left_window:right_window]))
return moving_avg | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/timeseries_errors.py#L8-L45 |
HDI-Project/MLPrimitives | mlprimitives/candidates/timeseries_errors.py | extract_anomalies | def extract_anomalies(y_true, smoothed_errors, window_size, batch_size, error_buffer):
"""
Extracts anomalies from the errors.
Args:
y_true ():
smoothed_errors ():
window_size (int):
batch_size (int):
error_buffer (int):
Returns:
"""
if len(y_true) <= batch_size * window_size:
raise ValueError("Window size (%s) larger than y_true (len=%s)."
% (batch_size, len(y_true)))
num_windows = int((len(y_true) - (batch_size * window_size)) / batch_size)
anomalies_indices = []
for i in range(num_windows + 1):
prev_index = i * batch_size
curr_index = (window_size * batch_size) + (i * batch_size)
if i == num_windows + 1:
curr_index = len(y_true)
window_smoothed_errors = smoothed_errors[prev_index:curr_index]
window_y_true = y_true[prev_index:curr_index]
epsilon, sd_threshold = compute_threshold(window_smoothed_errors, error_buffer)
window_anom_indices = get_anomalies(
window_smoothed_errors,
window_y_true,
sd_threshold,
i,
anomalies_indices,
error_buffer
)
# get anomalies from inverse of smoothed errors
# This was done in the implementation of NASA paper but
# wasn't referenced in the paper
# we get the inverse by flipping around the mean
mu = np.mean(window_smoothed_errors)
smoothed_errors_inv = [mu + (mu - e) for e in window_smoothed_errors]
epsilon_inv, sd_inv = compute_threshold(smoothed_errors_inv, error_buffer)
inv_anom_indices = get_anomalies(
smoothed_errors_inv,
window_y_true,
sd_inv,
i,
anomalies_indices,
len(y_true)
)
anomalies_indices = list(set(anomalies_indices + inv_anom_indices))
anomalies_indices.extend([i_a + i * batch_size for i_a in window_anom_indices])
# group anomalies
anomalies_indices = sorted(list(set(anomalies_indices)))
anomalies_groups = [list(group) for group in mit.consecutive_groups(anomalies_indices)]
anomaly_sequences = [(g[0], g[-1]) for g in anomalies_groups if not g[0] == g[-1]]
# generate "scores" for anomalies based on the max distance from epsilon for each sequence
anomalies_scores = []
for e_seq in anomaly_sequences:
denominator = np.mean(smoothed_errors) + np.std(smoothed_errors)
score = max([
abs(smoothed_errors[x] - epsilon) / denominator
for x in range(e_seq[0], e_seq[1])
])
anomalies_scores.append(score)
return anomaly_sequences, anomalies_scores | python | def extract_anomalies(y_true, smoothed_errors, window_size, batch_size, error_buffer):
if len(y_true) <= batch_size * window_size:
raise ValueError("Window size (%s) larger than y_true (len=%s)."
% (batch_size, len(y_true)))
num_windows = int((len(y_true) - (batch_size * window_size)) / batch_size)
anomalies_indices = []
for i in range(num_windows + 1):
prev_index = i * batch_size
curr_index = (window_size * batch_size) + (i * batch_size)
if i == num_windows + 1:
curr_index = len(y_true)
window_smoothed_errors = smoothed_errors[prev_index:curr_index]
window_y_true = y_true[prev_index:curr_index]
epsilon, sd_threshold = compute_threshold(window_smoothed_errors, error_buffer)
window_anom_indices = get_anomalies(
window_smoothed_errors,
window_y_true,
sd_threshold,
i,
anomalies_indices,
error_buffer
)
mu = np.mean(window_smoothed_errors)
smoothed_errors_inv = [mu + (mu - e) for e in window_smoothed_errors]
epsilon_inv, sd_inv = compute_threshold(smoothed_errors_inv, error_buffer)
inv_anom_indices = get_anomalies(
smoothed_errors_inv,
window_y_true,
sd_inv,
i,
anomalies_indices,
len(y_true)
)
anomalies_indices = list(set(anomalies_indices + inv_anom_indices))
anomalies_indices.extend([i_a + i * batch_size for i_a in window_anom_indices])
anomalies_indices = sorted(list(set(anomalies_indices)))
anomalies_groups = [list(group) for group in mit.consecutive_groups(anomalies_indices)]
anomaly_sequences = [(g[0], g[-1]) for g in anomalies_groups if not g[0] == g[-1]]
anomalies_scores = []
for e_seq in anomaly_sequences:
denominator = np.mean(smoothed_errors) + np.std(smoothed_errors)
score = max([
abs(smoothed_errors[x] - epsilon) / denominator
for x in range(e_seq[0], e_seq[1])
])
anomalies_scores.append(score)
return anomaly_sequences, anomalies_scores | [
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| Extracts anomalies from the errors.
Args:
y_true ():
smoothed_errors ():
window_size (int):
batch_size (int):
error_buffer (int):
Returns: | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/timeseries_errors.py#L48-L125 |
HDI-Project/MLPrimitives | mlprimitives/candidates/timeseries_errors.py | compute_threshold | def compute_threshold(smoothed_errors, error_buffer, sd_limit=12.0):
"""Helper method for `extract_anomalies` method.
Calculates the epsilon (threshold) for anomalies.
"""
mu = np.mean(smoothed_errors)
sigma = np.std(smoothed_errors)
max_epsilon = 0
sd_threshold = sd_limit
# The treshold is determined dynamically by testing multiple Zs.
# z is drawn from an ordered set of positive values representing the
# number of standard deviations above mean(smoothed_errors)
# here we iterate in increments of 0.5 on the range that the NASA paper found to be good
for z in np.arange(2.5, sd_limit, 0.5):
epsilon = mu + (sigma * z)
below_epsilon, below_indices, above_epsilon = [], [], []
for i in range(len(smoothed_errors)):
e = smoothed_errors[i]
if e < epsilon:
# save to compute delta mean and delta std
# these are important for epsilon calculation
below_epsilon.append(e)
below_indices.append(i)
if e > epsilon:
# above_epsilon values are anomalies
for j in range(0, error_buffer):
if (i + j) not in above_epsilon and (i + j) < len(smoothed_errors):
above_epsilon.append(i + j)
if (i - j) not in above_epsilon and (i - j) >= 0:
above_epsilon.append(i - j)
if len(above_epsilon) == 0:
continue
# generate sequences
above_epsilon = sorted(list(set(above_epsilon)))
groups = [list(group) for group in mit.consecutive_groups(above_epsilon)]
above_sequences = [(g[0], g[-1]) for g in groups if not g[0] == g[-1]]
mean_perc_decrease = (mu - np.mean(below_epsilon)) / mu
sd_perc_decrease = (sigma - np.std(below_epsilon)) / sigma
epsilon = (mean_perc_decrease + sd_perc_decrease) /\
(len(above_sequences)**2 + len(above_epsilon))
# update the largest epsilon we've seen so far
if epsilon > max_epsilon:
sd_threshold = z
max_epsilon = epsilon
# sd_threshold can be multiplied by sigma to get epsilon
return max_epsilon, sd_threshold | python | def compute_threshold(smoothed_errors, error_buffer, sd_limit=12.0):
mu = np.mean(smoothed_errors)
sigma = np.std(smoothed_errors)
max_epsilon = 0
sd_threshold = sd_limit
for z in np.arange(2.5, sd_limit, 0.5):
epsilon = mu + (sigma * z)
below_epsilon, below_indices, above_epsilon = [], [], []
for i in range(len(smoothed_errors)):
e = smoothed_errors[i]
if e < epsilon:
below_epsilon.append(e)
below_indices.append(i)
if e > epsilon:
for j in range(0, error_buffer):
if (i + j) not in above_epsilon and (i + j) < len(smoothed_errors):
above_epsilon.append(i + j)
if (i - j) not in above_epsilon and (i - j) >= 0:
above_epsilon.append(i - j)
if len(above_epsilon) == 0:
continue
above_epsilon = sorted(list(set(above_epsilon)))
groups = [list(group) for group in mit.consecutive_groups(above_epsilon)]
above_sequences = [(g[0], g[-1]) for g in groups if not g[0] == g[-1]]
mean_perc_decrease = (mu - np.mean(below_epsilon)) / mu
sd_perc_decrease = (sigma - np.std(below_epsilon)) / sigma
epsilon = (mean_perc_decrease + sd_perc_decrease) /\
(len(above_sequences)**2 + len(above_epsilon))
if epsilon > max_epsilon:
sd_threshold = z
max_epsilon = epsilon
return max_epsilon, sd_threshold | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/timeseries_errors.py#L128-L183 |
HDI-Project/MLPrimitives | mlprimitives/candidates/timeseries_errors.py | get_anomalies | def get_anomalies(smoothed_errors, y_true, z, window, all_anomalies, error_buffer):
"""
Helper method to get anomalies.
"""
mu = np.mean(smoothed_errors)
sigma = np.std(smoothed_errors)
epsilon = mu + (z * sigma)
# compare to epsilon
errors_seq, anomaly_indices, max_error_below_e = group_consecutive_anomalies(
smoothed_errors,
epsilon,
y_true,
error_buffer,
window,
all_anomalies
)
if len(errors_seq) > 0:
anomaly_indices = prune_anomalies(
errors_seq,
smoothed_errors,
max_error_below_e,
anomaly_indices
)
return anomaly_indices | python | def get_anomalies(smoothed_errors, y_true, z, window, all_anomalies, error_buffer):
mu = np.mean(smoothed_errors)
sigma = np.std(smoothed_errors)
epsilon = mu + (z * sigma)
errors_seq, anomaly_indices, max_error_below_e = group_consecutive_anomalies(
smoothed_errors,
epsilon,
y_true,
error_buffer,
window,
all_anomalies
)
if len(errors_seq) > 0:
anomaly_indices = prune_anomalies(
errors_seq,
smoothed_errors,
max_error_below_e,
anomaly_indices
)
return anomaly_indices | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/timeseries_errors.py#L186-L214 |
HDI-Project/MLPrimitives | mlprimitives/candidates/timeseries_errors.py | prune_anomalies | def prune_anomalies(e_seq, smoothed_errors, max_error_below_e, anomaly_indices):
""" Helper method that removes anomalies which don't meet
a minimum separation from next anomaly.
"""
# min accepted perc decrease btwn max errors in anomalous sequences
MIN_PERCENT_DECREASE = 0.05
e_seq_max, smoothed_errors_max = [], []
for error_seq in e_seq:
if len(smoothed_errors[error_seq[0]:error_seq[1]]) > 0:
sliced_errors = smoothed_errors[error_seq[0]:error_seq[1]]
e_seq_max.append(max(sliced_errors))
smoothed_errors_max.append(max(sliced_errors))
smoothed_errors_max.sort(reverse=True)
if max_error_below_e > 0:
smoothed_errors_max.append(max_error_below_e)
indices_remove = []
for i in range(len(smoothed_errors_max)):
if i < len(smoothed_errors_max) - 1:
delta = smoothed_errors_max[i] - smoothed_errors_max[i + 1]
perc_change = delta / smoothed_errors_max[i]
if perc_change < MIN_PERCENT_DECREASE:
indices_remove.append(e_seq_max.index(smoothed_errors_max[i]))
for index in sorted(indices_remove, reverse=True):
del e_seq[index]
pruned_indices = []
for i in anomaly_indices:
for error_seq in e_seq:
if i >= error_seq[0] and i <= error_seq[1]:
pruned_indices.append(i)
return pruned_indices | python | def prune_anomalies(e_seq, smoothed_errors, max_error_below_e, anomaly_indices):
MIN_PERCENT_DECREASE = 0.05
e_seq_max, smoothed_errors_max = [], []
for error_seq in e_seq:
if len(smoothed_errors[error_seq[0]:error_seq[1]]) > 0:
sliced_errors = smoothed_errors[error_seq[0]:error_seq[1]]
e_seq_max.append(max(sliced_errors))
smoothed_errors_max.append(max(sliced_errors))
smoothed_errors_max.sort(reverse=True)
if max_error_below_e > 0:
smoothed_errors_max.append(max_error_below_e)
indices_remove = []
for i in range(len(smoothed_errors_max)):
if i < len(smoothed_errors_max) - 1:
delta = smoothed_errors_max[i] - smoothed_errors_max[i + 1]
perc_change = delta / smoothed_errors_max[i]
if perc_change < MIN_PERCENT_DECREASE:
indices_remove.append(e_seq_max.index(smoothed_errors_max[i]))
for index in sorted(indices_remove, reverse=True):
del e_seq[index]
pruned_indices = []
for i in anomaly_indices:
for error_seq in e_seq:
if i >= error_seq[0] and i <= error_seq[1]:
pruned_indices.append(i)
return pruned_indices | [
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| train | https://github.com/HDI-Project/MLPrimitives/blob/bf415f9f751724ff545a1156ddfd7524e320f469/mlprimitives/candidates/timeseries_errors.py#L262-L299 |
ultrabug/uhashring | uhashring/ring.py | HashRing._configure_nodes | def _configure_nodes(self, nodes):
"""Parse and set up the given nodes.
:param nodes: nodes used to create the continuum (see doc for format).
"""
if isinstance(nodes, str):
nodes = [nodes]
elif not isinstance(nodes, (dict, list)):
raise ValueError(
'nodes configuration should be a list or a dict,'
' got {}'.format(type(nodes)))
conf_changed = False
for node in nodes:
conf = {
'hostname': node,
'instance': None,
'nodename': node,
'port': None,
'vnodes': self._default_vnodes,
'weight': 1
}
current_conf = self.runtime._nodes.get(node, {})
nodename = node
# new node, trigger a ring update
if not current_conf:
conf_changed = True
# complex config
if isinstance(nodes, dict):
node_conf = nodes[node]
if isinstance(node_conf, int):
conf['weight'] = node_conf
elif isinstance(node_conf, dict):
for k, v in node_conf.items():
if k in conf:
conf[k] = v
# changing those config trigger a ring update
if k in ['nodename', 'vnodes', 'weight']:
if current_conf.get(k) != v:
conf_changed = True
else:
raise ValueError(
'node configuration should be a dict or an int,'
' got {}'.format(type(node_conf)))
if self._weight_fn:
conf['weight'] = self._weight_fn(**conf)
# changing the weight of a node trigger a ring update
if current_conf.get('weight') != conf['weight']:
conf_changed = True
self.runtime._nodes[nodename] = conf
return conf_changed | python | def _configure_nodes(self, nodes):
if isinstance(nodes, str):
nodes = [nodes]
elif not isinstance(nodes, (dict, list)):
raise ValueError(
'nodes configuration should be a list or a dict,'
' got {}'.format(type(nodes)))
conf_changed = False
for node in nodes:
conf = {
'hostname': node,
'instance': None,
'nodename': node,
'port': None,
'vnodes': self._default_vnodes,
'weight': 1
}
current_conf = self.runtime._nodes.get(node, {})
nodename = node
if not current_conf:
conf_changed = True
if isinstance(nodes, dict):
node_conf = nodes[node]
if isinstance(node_conf, int):
conf['weight'] = node_conf
elif isinstance(node_conf, dict):
for k, v in node_conf.items():
if k in conf:
conf[k] = v
if k in ['nodename', 'vnodes', 'weight']:
if current_conf.get(k) != v:
conf_changed = True
else:
raise ValueError(
'node configuration should be a dict or an int,'
' got {}'.format(type(node_conf)))
if self._weight_fn:
conf['weight'] = self._weight_fn(**conf)
if current_conf.get('weight') != conf['weight']:
conf_changed = True
self.runtime._nodes[nodename] = conf
return conf_changed | [
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| train | https://github.com/ultrabug/uhashring/blob/2297471a392e28ed913b3276c2f48d0c01523375/uhashring/ring.py#L44-L94 |
ultrabug/uhashring | uhashring/ring.py | HashRing._get_pos | def _get_pos(self, key):
"""Get the index of the given key in the sorted key list.
We return the position with the nearest hash based on
the provided key unless we reach the end of the continuum/ring
in which case we return the 0 (beginning) index position.
:param key: the key to hash and look for.
"""
p = bisect(self.runtime._keys, self.hashi(key))
if p == len(self.runtime._keys):
return 0
else:
return p | python | def _get_pos(self, key):
p = bisect(self.runtime._keys, self.hashi(key))
if p == len(self.runtime._keys):
return 0
else:
return p | [
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We return the position with the nearest hash based on
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:param key: the key to hash and look for. | [
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| train | https://github.com/ultrabug/uhashring/blob/2297471a392e28ed913b3276c2f48d0c01523375/uhashring/ring.py#L125-L138 |
ultrabug/uhashring | uhashring/ring.py | HashRing._get | def _get(self, key, what):
"""Generic getter magic method.
The node with the nearest but not less hash value is returned.
:param key: the key to look for.
:param what: the information to look for in, allowed values:
- instance (default): associated node instance
- nodename: node name
- pos: index of the given key in the ring
- tuple: ketama compatible (pos, name) tuple
- weight: node weight
"""
if not self.runtime._ring:
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pos = self._get_pos(key)
if what == 'pos':
return pos
nodename = self.runtime._ring[self.runtime._keys[pos]]
if what in ['hostname', 'instance', 'port', 'weight']:
return self.runtime._nodes[nodename][what]
elif what == 'dict':
return self.runtime._nodes[nodename]
elif what == 'nodename':
return nodename
elif what == 'tuple':
return (self.runtime._keys[pos], nodename) | python | def _get(self, key, what):
if not self.runtime._ring:
return None
pos = self._get_pos(key)
if what == 'pos':
return pos
nodename = self.runtime._ring[self.runtime._keys[pos]]
if what in ['hostname', 'instance', 'port', 'weight']:
return self.runtime._nodes[nodename][what]
elif what == 'dict':
return self.runtime._nodes[nodename]
elif what == 'nodename':
return nodename
elif what == 'tuple':
return (self.runtime._keys[pos], nodename) | [
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- instance (default): associated node instance
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- tuple: ketama compatible (pos, name) tuple
- weight: node weight | [
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| train | https://github.com/ultrabug/uhashring/blob/2297471a392e28ed913b3276c2f48d0c01523375/uhashring/ring.py#L140-L168 |
ultrabug/uhashring | uhashring/ring.py | HashRing.get_instances | def get_instances(self):
"""Returns a list of the instances of all the configured nodes.
"""
return [c.get('instance') for c in self.runtime._nodes.values()
if c.get('instance')] | python | def get_instances(self):
return [c.get('instance') for c in self.runtime._nodes.values()
if c.get('instance')] | [
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| train | https://github.com/ultrabug/uhashring/blob/2297471a392e28ed913b3276c2f48d0c01523375/uhashring/ring.py#L177-L181 |
ultrabug/uhashring | uhashring/ring.py | HashRing.get_points | def get_points(self):
"""Returns a ketama compatible list of (position, nodename) tuples.
"""
return [(k, self.runtime._ring[k]) for k in self.runtime._keys] | python | def get_points(self):
return [(k, self.runtime._ring[k]) for k in self.runtime._keys] | [
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| train | https://github.com/ultrabug/uhashring/blob/2297471a392e28ed913b3276c2f48d0c01523375/uhashring/ring.py#L232-L235 |
ultrabug/uhashring | uhashring/ring.py | HashRing.iterate_nodes | def iterate_nodes(self, key, distinct=True):
"""hash_ring compatibility implementation.
Given a string key it returns the nodes as a generator that
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The generator iterates one time through the ring
starting at the correct position.
if `distinct` is set, then the nodes returned will be unique,
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"""
if not self.runtime._ring:
yield None
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for node in self.range(key, unique=distinct):
yield node['nodename'] | python | def iterate_nodes(self, key, distinct=True):
if not self.runtime._ring:
yield None
else:
for node in self.range(key, unique=distinct):
yield node['nodename'] | [
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| train | https://github.com/ultrabug/uhashring/blob/2297471a392e28ed913b3276c2f48d0c01523375/uhashring/ring.py#L244-L258 |
ultrabug/uhashring | uhashring/ring.py | HashRing.print_continuum | def print_continuum(self):
"""Prints a ketama compatible continuum report.
"""
numpoints = len(self.runtime._keys)
if numpoints:
print('Numpoints in continuum: {}'.format(numpoints))
else:
print('Continuum empty')
for p in self.get_points():
point, node = p
print('{} ({})'.format(node, point)) | python | def print_continuum(self):
numpoints = len(self.runtime._keys)
if numpoints:
print('Numpoints in continuum: {}'.format(numpoints))
else:
print('Continuum empty')
for p in self.get_points():
point, node = p
print('{} ({})'.format(node, point)) | [
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| train | https://github.com/ultrabug/uhashring/blob/2297471a392e28ed913b3276c2f48d0c01523375/uhashring/ring.py#L260-L270 |
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