Final Baird Commit 2
This commit is contained in:
Alexander Rey
parent
8a78279eaa
commit
8745f85dca
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@ -0,0 +1,220 @@
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import requests
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import json
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import time
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import xmltodict
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import datetime
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from bs4 import BeautifulSoup
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import secret
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loginUrl = ('https://api.loginradius.com/identity/v2/auth/login?apiKey=d842e884-2dfb-4c8f-a971-f9eacf8e9f54&loginUrl=&emailTemplate=Verification English&verificationUrl=https://account.hydroottawa.com/login')
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authenticatorUrl = ('https://account.hydroottawa.com/ajax/authenticator')
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secureUrl = ('https://account.hydroottawa.com/')
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downloadDataUrl = ('https://secure.hydroottawa.com/Usage/Secure/TOU/DownloadMyData.aspx')
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ssoMhlUrl = ('https://account.hydroottawa.com/ajax/sso_mhl')
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ssoTargetUrl = ('https://secure.hydroottawa.com/Usage/SSO/SSOTarget.aspx')
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jsonPayload = {
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'email': secret.email,
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'password': secret.password
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}
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headersPayloadOptions = {
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'referrer': secureUrl,
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'origin': secureUrl,
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'Access-Control-Request-Method': 'POST',
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'Connection': 'keep-alive'
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}
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headersPayload = {
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'referrer': secureUrl,
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'origin': secureUrl,
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'Connection': 'keep-alive',
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'Content-Type': 'application/json'
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}
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with requests.session() as s:
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r = s.get(secureUrl)
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print('get: ', r)
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r = s.options(loginUrl, headers=headersPayloadOptions)
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print('options: ', r)
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r = s.post(loginUrl, json=jsonPayload, headers=headersPayload)
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print('login post: ', r)
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responseJson = r.json()
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payload = {
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'source': 'login',
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'uid': responseJson['Profile']['Uid'],
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'token': responseJson['access_token'],
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'expires': responseJson['expires_in'],
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'profile[Identities]': responseJson['Profile']['Identities'],
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'profile[Password]': responseJson['Profile']['Password'],
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'profile[LastPasswordChangeDate]': responseJson['Profile']['LastPasswordChangeDate'],
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'profile[PasswordExpirationDate]': responseJson['Profile']['PasswordExpirationDate'],
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'profile[LastPasswordChangeToken]': responseJson['Profile']['LastPasswordChangeToken'],
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'profile[EmailVerified]': responseJson['Profile']['EmailVerified'],
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'profile[IsActive]': responseJson['Profile']['IsActive'],
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'profile[IsDeleted]': responseJson['Profile']['IsDeleted'],
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'profile[Uid]': responseJson['Profile']['Uid'],
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'profile[CustomFields][EBilling]': responseJson['Profile']['CustomFields']['EBilling'],
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'profile[CustomFields][AccountID]': responseJson['Profile']['CustomFields']['AccountID'],
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'profile[CustomFields][PreviousBillAmount]': responseJson['Profile']['CustomFields']['PreviousBillAmount'],
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'profile[CustomFields][PreviousDueDate]': responseJson['Profile']['CustomFields']['PreviousDueDate'],
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'profile[IsEmailSubscribed]': responseJson['Profile']['IsEmailSubscribed'],
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'profile[UserName]': responseJson['Profile']['UserName'],
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'profile[NoOfLogins]': responseJson['Profile']['NoOfLogins'],
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'profile[PhoneId]': responseJson['Profile']['PhoneId'],
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'profile[PhoneIdVerified]': responseJson['Profile']['PhoneIdVerified'],
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'profile[Roles]': responseJson['Profile']['Roles'],
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'profile[ExternalUserLoginId]': responseJson['Profile']['ExternalUserLoginId'],
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'profile[RegistrationProvider]': responseJson['Profile']['RegistrationProvider'],
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'profile[IsLoginLocked]': responseJson['Profile']['IsLoginLocked'],
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'profile[LoginLockedType]': responseJson['Profile']['LoginLockedType'],
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'profile[LastLoginLocation]': responseJson['Profile']['LastLoginLocation'],
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'profile[RegistrationSource]': responseJson['Profile']['RegistrationSource'],
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'profile[IsCustomUid]': responseJson['Profile']['IsCustomUid'],
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'profile[UnverifiedEmail]': responseJson['Profile']['UnverifiedEmail'],
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'profile[IsSecurePassword]': responseJson['Profile']['IsSecurePassword'],
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'profile[PrivacyPolicy]': responseJson['Profile']['PrivacyPolicy'],
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'profile[ExternalIds]': responseJson['Profile']['ExternalIds'],
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'profile[IsRequiredFieldsFilledOnce]': responseJson['Profile']['IsRequiredFieldsFilledOnce'],
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'profile[ConsentProfile]': responseJson['Profile']['ConsentProfile'],
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'profile[PIN]': responseJson['Profile']['PIN'],
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'profile[RegistrationData]': responseJson['Profile']['RegistrationData'],
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'profile[ID]': responseJson['Profile']['ID'],
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'profile[Provider]': responseJson['Profile']['Provider'],
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'profile[Prefix]': responseJson['Profile']['Prefix'],
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'profile[FirstName]': responseJson['Profile']['FirstName'],
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'profile[MiddleName]': responseJson['Profile']['MiddleName'],
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'profile[LastName]': responseJson['Profile']['LastName'],
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'profile[Suffix]': responseJson['Profile']['Suffix'],
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'profile[FullName]': responseJson['Profile']['FullName'],
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'profile[NickName]': responseJson['Profile']['NickName'],
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'profile[ProfileName]': responseJson['Profile']['ProfileName'],
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'profile[BirthDate]': responseJson['Profile']['BirthDate'],
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'profile[Gender]': responseJson['Profile']['Gender'],
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'profile[Website]': responseJson['Profile']['Website'],
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'profile[Email][0][Type]': responseJson['Profile']['Email'][0]['Type'],
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'profile[Email][0][Value]': responseJson['Profile']['Email'][0]['Value'],
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'profile[Country]': responseJson['Profile']['Country'],
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'profile[ThumbnailImageUrl]': responseJson['Profile']['ThumbnailImageUrl'],
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'profile[ImageUrl]': responseJson['Profile']['ImageUrl'],
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'profile[Favicon]': responseJson['Profile']['Favicon'],
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'profile[ProfileUrl]': responseJson['Profile']['ProfileUrl'],
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'profile[HomeTown]': responseJson['Profile']['HomeTown'],
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'profile[State]': responseJson['Profile']['State'],
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'profile[City]': responseJson['Profile']['City'],
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'profile[Industry]': responseJson['Profile']['Industry'],
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'profile[About]': responseJson['Profile']['About'],
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'profile[TimeZone]': responseJson['Profile']['TimeZone'],
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'profile[LocalLanguage]': responseJson['Profile']['LocalLanguage'],
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'profile[CoverPhoto]': responseJson['Profile']['CoverPhoto'],
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'profile[TagLine]': responseJson['Profile']['TagLine'],
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'profile[Language]': responseJson['Profile']['Language'],
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'profile[Verified]': responseJson['Profile']['Verified'],
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'profile[UpdatedTime]': responseJson['Profile']['UpdatedTime'],
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'profile[Positions]': responseJson['Profile']['Positions'],
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'profile[Educations]': responseJson['Profile']['Educations'],
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'profile[PhoneNumbers]': responseJson['Profile']['PhoneNumbers'],
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'profile[IMAccounts]': responseJson['Profile']['IMAccounts'],
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'profile[Addresses]': responseJson['Profile']['Addresses'],
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'profile[MainAddress]': responseJson['Profile']['MainAddress'],
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'profile[Created]': responseJson['Profile']['Created'],
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'profile[CreatedDate]': responseJson['Profile']['CreatedDate'],
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'profile[ModifiedDate]': responseJson['Profile']['ModifiedDate'],
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'profile[ProfileModifiedDate]': responseJson['Profile']['ProfileModifiedDate'],
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'profile[LocalCity]': responseJson['Profile']['LocalCity'],
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'profile[ProfileCity]': responseJson['Profile']['ProfileCity'],
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'profile[LocalCountry]': responseJson['Profile']['LocalCountry'],
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'profile[ProfileCountry]': responseJson['Profile']['ProfileCountry'],
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'profile[FirstLogin]': responseJson['Profile']['FirstLogin'],
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'profile[IsProtected]': responseJson['Profile']['IsProtected'],
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'profile[RelationshipStatus]': responseJson['Profile']['RelationshipStatus'],
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'profile[Quota]': responseJson['Profile']['Quota'],
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'profile[Quote]': responseJson['Profile']['Quote'],
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'profile[InterestedIn]': responseJson['Profile']['InterestedIn'],
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'profile[Interests]': responseJson['Profile']['Interests'],
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'profile[Religion]': responseJson['Profile']['Religion'],
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'profile[Political]': responseJson['Profile']['Political'],
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'profile[Sports]': responseJson['Profile']['Sports'],
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'profile[InspirationalPeople]': responseJson['Profile']['InspirationalPeople'],
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'profile[HttpsImageUrl]': responseJson['Profile']['HttpsImageUrl'],
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'profile[FollowersCount]': responseJson['Profile']['FollowersCount'],
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'profile[FriendsCount]': responseJson['Profile']['FriendsCount'],
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'profile[IsGeoEnabled]': responseJson['Profile']['IsGeoEnabled'],
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'profile[TotalStatusesCount]': responseJson['Profile']['TotalStatusesCount'],
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'profile[Associations]': responseJson['Profile']['Associations'],
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'profile[NumRecommenders]': responseJson['Profile']['NumRecommenders'],
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'profile[Honors]': responseJson['Profile']['Honors'],
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'profile[Awards]': responseJson['Profile']['Awards'],
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'profile[Skills]': responseJson['Profile']['Skills'],
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'profile[CurrentStatus]': responseJson['Profile']['CurrentStatus'],
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'profile[Certifications]': responseJson['Profile']['Certifications'],
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'profile[Courses]': responseJson['Profile']['Courses'],
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'profile[Volunteer]': responseJson['Profile']['Volunteer'],
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'profile[RecommendationsReceived]': responseJson['Profile']['RecommendationsReceived'],
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'profile[Languages]': responseJson['Profile']['Languages'],
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'profile[Projects]': responseJson['Profile']['Projects'],
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'profile[Games]': responseJson['Profile']['Games'],
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'profile[Family]': responseJson['Profile']['Family'],
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'profile[TeleVisionShow]': responseJson['Profile']['TeleVisionShow'],
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'profile[MutualFriends]': responseJson['Profile']['MutualFriends'],
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'profile[Movies]': responseJson['Profile']['Movies'],
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'profile[Books]': responseJson['Profile']['Books'],
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'profile[AgeRange]': responseJson['Profile']['AgeRange'],
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'profile[PublicRepository]': responseJson['Profile']['PublicRepository'],
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'profile[Hireable]': responseJson['Profile']['Hireable'],
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'profile[RepositoryUrl]': responseJson['Profile']['RepositoryUrl'],
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'profile[Age]': responseJson['Profile']['Age'],
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'profile[Patents]': responseJson['Profile']['Patents'],
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'profile[FavoriteThings]': responseJson['Profile']['FavoriteThings'],
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'profile[ProfessionalHeadline]': responseJson['Profile']['ProfessionalHeadline'],
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'profile[ProviderAccessCredential]': responseJson['Profile']['ProviderAccessCredential'],
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'profile[RelatedProfileViews]': responseJson['Profile']['RelatedProfileViews'],
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'profile[KloutScore]': responseJson['Profile']['KloutScore'],
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'profile[LRUserID]': responseJson['Profile']['LRUserID'],
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'profile[PlacesLived]': responseJson['Profile']['PlacesLived'],
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'profile[Publications]': responseJson['Profile']['Publications'],
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'profile[JobBookmarks]': responseJson['Profile']['JobBookmarks'],
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'profile[Suggestions]': responseJson['Profile']['Suggestions'],
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'profile[Badges]': responseJson['Profile']['Badges'],
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'profile[MemberUrlResources]': responseJson['Profile']['MemberUrlResources'],
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'profile[TotalPrivateRepository]': responseJson['Profile']['TotalPrivateRepository'],
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'profile[Currency]': responseJson['Profile']['Currency'],
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'profile[StarredUrl]': responseJson['Profile']['StarredUrl'],
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'profile[GistsUrl]': responseJson['Profile']['GistsUrl'],
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'profile[PublicGists]': responseJson['Profile']['PublicGists'],
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'profile[PrivateGists]': responseJson['Profile']['PrivateGists'],
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'profile[Subscription]': responseJson['Profile']['Subscription'],
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'profile[Company]': responseJson['Profile']['Company'],
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'profile[GravatarImageUrl]': responseJson['Profile']['GravatarImageUrl'],
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'profile[ProfileImageUrls]': responseJson['Profile']['ProfileImageUrls'],
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'profile[WebProfiles]': responseJson['Profile']['WebProfiles'],
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'profile[PinsCount]': responseJson['Profile']['PinsCount'],
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'profile[BoardsCount]': responseJson['Profile']['BoardsCount'],
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'profile[LikesCount]': responseJson['Profile']['LikesCount'],
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'profile[SignupDate]': responseJson['Profile']['SignupDate'],
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'profile[LastLoginDate]': responseJson['Profile']['LastLoginDate'],
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'profile[PreviousUids]': responseJson['Profile']['PreviousUids']
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}
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print('Number of logins: ', responseJson['Profile']['NoOfLogins'])
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r = s.post(authenticatorUrl, data=payload)
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print('authenticator post: ', r)
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r = s.get(secureUrl)
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print('succesfully logged in')
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ssoMhlPayload = {
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'account': responseJson['Profile']['CustomFields']['AccountID'],
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'request_type': 'BILLING'
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}
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r = s.post(ssoMhlUrl, data=ssoMhlPayload)
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print('SSO: ', r)
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responseSsoMhlJson = r.json()
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ssoTargetPayload = {
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'authssotoken': responseSsoMhlJson['token']
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}
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r = s.post(ssoTargetUrl, data=ssoTargetPayload)
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print('SSO Target: ', r)
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r = s.get(downloadDataUrl)
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print('Download My Data: ', r)
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# TODO: Need to figure out how to read data
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@ -0,0 +1,2 @@
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email = "alexander.rey1@gmail.com"
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password = "7hW02#t03!"
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@ -2,7 +2,7 @@
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="jdk" jdkName="Python 3.8 (dfm_tools_env)" jdkType="Python SDK" />
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<orderEntry type="jdk" jdkName="Python 3.9 (dfm_tools_23)" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<component name="PyDocumentationSettings">
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@ -1,4 +1,4 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (dfm_tools_env)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (dfm_tools_23)" project-jdk-type="Python SDK" />
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</project>
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@ -15,8 +15,11 @@ import datetime as datetime
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from scipy.interpolate import LinearNDInterpolator, interp1d
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import dfm_tools as dfmt
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from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
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from dfm_tools.get_nc_helpers import get_timesfromnc, get_ncfilelist, get_hisstationlist, get_ncvardimlist, get_timesfromnc
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from dfm_tools.get_nc_helpers import get_ncvarproperties, get_stationid_fromstationlist, get_varnamefromattrs
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import cartopy.crs as ccrs
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import contextily as ctx
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from dfm_tools.regulargrid import scatter_to_regulargrid
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@ -64,7 +64,7 @@ dataPath = "FlowFM_map.nc"
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#%% Import using DFM functions
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#modelPlot = [35, 36, 37, 39, 40, 41, 42, 43, 44, 45]
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modelPlot = [18, 35]
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modelPlot = [18, 35, 41, 46, 47, 48, 49]
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dataIN_gp = [None] * (max(modelPlot)+1)
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dataOUT = [None] * (max(modelPlot)+1)
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@ -181,14 +181,14 @@ ax = axes.flat
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modelPlot = [35, 37, 40, 41, 42, 43, 44]
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modelPlot = [40, 41, 42, 43, 44]
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modelPlot = [35, 37, 40, 45]
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modelPlot = [18, 35]
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modelPlot = [41, 47, 48, 49]
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for i in modelPlot:
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# S1
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ax[0].set_title('Water Level')
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ax[0].plot(dataOUT[i].index, dataOUT[i]['s1'], linewidth=3, label=runLog['Run Title'][i])
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ax[0].set_ylim([330, 345])
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ax[0].set_ylim([325, 357])
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# ax[0].set_xlim([0, 10000])
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ax[0].set_xlim([0, 90000])
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@ -198,7 +198,7 @@ for i in modelPlot:
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# Shear
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ax[1].set_title('Shear Stress')
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ax[1].plot(dataOUT[i].index, dataOUT[i]['taus'], linewidth=3, label=runLog['Run Title'][i])
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ax[1].set_ylim([0, 2])
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ax[1].set_ylim([0, 0.25])
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ax[1].legend(loc='upper right')
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ax[1].set_ylabel('Shear Stress [N/m^2]')
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@ -233,8 +233,8 @@ plt.show()
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# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/12_WAQ/FullDomainFigures/DomainHDCompare.png',
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# bbox_inches='tight', dpi=200)
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fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/07_PostProcessing/HD_Profile_Compare.png',
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bbox_inches='tight', dpi=200)
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# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/07_PostProcessing/HD_Profile_Compare.png',
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# bbox_inches='tight', dpi=200)
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#%% Plot select data along centerline
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fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(9, 6), sharex=True)
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@ -594,8 +594,8 @@ ax[1].set_xlim([0, 100000])
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#DetC in sediment
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ax[2].set_title('DetC (Fast) in Sediment')
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ax[2].plot(dataOUT.index, dataOUT['DetCS1']
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/ (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1'])
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, linewidth=3, label='DetCS1')
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/ (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
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linewidth=3, label='DetCS1')
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ax[2].legend()
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ax[2].set_ylabel('DetCS1 [g/g]')
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@ -0,0 +1,90 @@
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#%% Script to read and work with Access database
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import pyodbc
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import pandas as pd
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import geopandas as gpd
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#%% Establish a connection to the Access database
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conn_str = r'DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/Wood English-Wabigoon Export_alldata_2017-current.accdb;'
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conn = pyodbc.connect(conn_str)
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# Get the list of tables
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tables = conn.cursor().tables()
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# Save a list of the table names
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table_names = [table.table_name for table in tables]
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#%% Print rows from a table
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cursor = conn.cursor()
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cursor.execute('SELECT * FROM [General_Query]')
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for row in cursor.fetchall():
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print(row)
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#%% Print the column names
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cursor = conn.cursor()
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cursor.execute('SELECT * FROM [General_Query]')
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columns = [column[0] for column in cursor.description]
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print(columns)
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#%% Save the table to a pandas dataframe
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df = pd.read_sql_query('SELECT * FROM [General_Query]', conn)
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#%% Save the table to a pandas dataframe
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df_Samples = pd.read_sql_query('SELECT * FROM [EW_Export_Samples]', conn)
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#%% Save the table to a pandas dataframe
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df_Results = pd.read_sql_query('SELECT * FROM [EW_Export_Results]', conn)
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#%% Save the table to a pandas dataframe
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df_Locations = pd.read_sql_query('SELECT * FROM [EW_Export_Locations]', conn)
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#%% Close the connection
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conn.close()
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#%% Select a subset of the data where the media is Surface Water ('SW') and the param_name is 'Mercury' and the fraction is 'T'
|
||||
df_SW_Hg = df.loc[(df['media'] == 'SW') & (df['param_name'] == 'Mercury') & (df['fraction'] == 'T'), ['Long_coord', 'Lat_coord', 'ppm_result', 'ppm_uom', 'field_sample_date', 'param_name', 'tech_task_name']]
|
||||
|
||||
# Convert to geo dataframe using Lat and Long
|
||||
gdf_SW_Hg = gpd.GeoDataFrame(df_SW_Hg, geometry=gpd.points_from_xy(df_SW_Hg.Long_coord, df_SW_Hg.Lat_coord, crs="EPSG:4326"))
|
||||
|
||||
# Convert result to numeric
|
||||
gdf_SW_Hg['ppm_result'] = pd.to_numeric(gdf_SW_Hg['ppm_result'])
|
||||
|
||||
# Where ppm_uom is 'MG/L', convert to NG/L
|
||||
gdf_SED_Hg.loc[gdf_SED_Hg['ppm_uom'] == 'MG/L', 'ppm_result'] = gdf_SED_Hg['ppm_result'] * 1000000
|
||||
|
||||
# Drop where no result or location
|
||||
gdf_SW_Hg = gdf_SW_Hg.dropna(subset=['ppm_result', 'Long_coord', 'Lat_coord'])
|
||||
|
||||
# Convert datetime to string for shapefile
|
||||
gdf_SW_Hg['field_sample_date'] = gdf_SW_Hg['field_sample_date'].dt.strftime('%Y-%m')
|
||||
|
||||
# Save as a shapefile
|
||||
gdf_SW_Hg.to_file('C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/EW_Export_Samples_SW_Hg_RevB.shp')
|
||||
|
||||
|
||||
|
||||
#%% Select a subset of the data where:
|
||||
# - media is Sediment ('SED')
|
||||
# - the param_name is 'Mercury'
|
||||
# - The top depth is 0
|
||||
|
||||
df_SED_Hg = df.loc[(df['media'] == 'SED') & (df['param_name'] == 'Mercury') & (df['top_depth'] == 0), ['Long_coord', 'Lat_coord', 'ppm_result', 'ppm_uom']]
|
||||
|
||||
# Convert to geo dataframe using Lat and Long
|
||||
gdf_SED_Hg = gpd.GeoDataFrame(df_SED_Hg, geometry=gpd.points_from_xy(df_SED_Hg.Long_coord, df_SED_Hg.Lat_coord, crs="EPSG:4326"))
|
||||
|
||||
# Convert result to numeric
|
||||
gdf_SED_Hg['ppm_result'] = pd.to_numeric(gdf_SED_Hg['ppm_result'])
|
||||
|
||||
# Where ppm_uom is 'MG/KG', convert to NG/G
|
||||
gdf_SED_Hg.loc[gdf_SED_Hg['ppm_uom'] == 'MG/KG', 'ppm_result'] = gdf_SED_Hg['ppm_result'] * 1000
|
||||
|
||||
# Drop rows with no locatuion
|
||||
gdf_SED_Hg = gdf_SED_Hg.dropna(subset=['Long_coord', 'Lat_coord'])
|
||||
|
||||
# Save as a shapefile
|
||||
df_SW_Hg.to_file('C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/EW_Export_Samples_SW_Hg.shp')
|
||||
|
|
@ -26,6 +26,8 @@ dataIN_gp = gp.GeoDataFrame(df_in, geometry=gp.points_from_xy(df_in.Longitude, d
|
|||
# Convert to UTM
|
||||
dataIN_gp.to_crs(crs='EPSG:32615', inplace=True)
|
||||
|
||||
|
||||
|
||||
#%% Read in centerline shapefile
|
||||
river_centerline = gp.read_file('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/03_Data/02_Physical/16_Waterline/Centerline_for_Modelling_UTMZ15.shp')
|
||||
|
||||
|
|
|
|||
|
|
@ -42,9 +42,22 @@ river_centerline_merge_gpd2.iloc[0, 2] = 0
|
|||
|
||||
|
||||
#%% Read in combined dataset
|
||||
obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/combined dataset-SGJ.csv")
|
||||
# obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/combined dataset-SGJ.csv")
|
||||
# obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/combined dataset.csv")
|
||||
# obs_IN = pd.read_csv("C:/Users/arey/Downloads/ExportDownloads_EwrrpDataExport_rharris_2023-05-12_104911/EwrrpDataExport_rharris_2023-05-12_104911.txt", sep='\t', encoding='UTF-16 LE')
|
||||
df = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Results.txt", sep = '\t', encoding='UTF-16 LE')
|
||||
locations = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Sites.txt", sep = '\t', encoding='UTF-16 LE')
|
||||
samples = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Samples.txt", sep = '\t', encoding='UTF-16 LE')
|
||||
|
||||
#%% Merge locations and results
|
||||
obs_IN = pd.merge(left=df, right=locations, left_on= 'Samplesiteid', right_on= 'Id', how = 'left') # 1096 locations w/Hg
|
||||
obs_IN = pd.merge(left=obs_IN, right=samples, left_on= 'Sampleid', right_on= 'Id', how = 'left') # 1096 locations w/Hg
|
||||
|
||||
|
||||
# Add Datetime
|
||||
obs_IN['DateTime'] = pd.to_datetime(obs_IN.Sampledate_x)
|
||||
obs_IN['Sampledate'] = pd.to_datetime(obs_IN.Sampledate)
|
||||
|
||||
|
||||
|
||||
#%% Process combined datsaset
|
||||
# Convert to geodataframe
|
||||
|
|
@ -54,11 +67,11 @@ obs_gdf = gp.GeoDataFrame(obs_IN, geometry=gp.points_from_xy(
|
|||
obs_gdf = gp.sjoin_nearest(river_centerline_merge_gpd2, obs_gdf, how='right', max_distance=250).reset_index()
|
||||
|
||||
# Sediment Hg GeoDataFrame where media is Sediment
|
||||
obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
||||
obsMask = (((obs_gdf.Media == 'SED') | (obs_gdf.Media == 'SOIL')) &
|
||||
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
|
||||
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna() &
|
||||
((obs_gdf.TopDepth_x == 0) | (obs_gdf.TopDepth_x.isna())))
|
||||
(obs_gdf.Sampledate > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna() &
|
||||
((obs_gdf.TopDepth == 0) | (obs_gdf.TopDepth.isna())))
|
||||
# obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
||||
# ((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
|
||||
# | (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
|
|
@ -74,6 +87,42 @@ obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed
|
|||
obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Samplevalue'] * 0.001
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Samplevalue'] * 0.001
|
||||
|
||||
|
||||
|
||||
# Save subset of columns as a shapefile
|
||||
obs_HgSed.loc[:, ['Sample_NG/G', 'Sampledate_x', 'RiverKM', 'Latitude', 'Longitude', 'geometry',
|
||||
'Surveyname','Sitecode','Parameter']].to_file(
|
||||
'C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/BairdDB_HgSed.shp')
|
||||
|
||||
|
||||
#%% Extract Hg data from surface water samples
|
||||
|
||||
# Water Hg GeoDataFrame where media is SurfaceWater (SW)
|
||||
obsMask = (((obs_gdf.Media == 'SW')) &
|
||||
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)-Total')
|
||||
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
(obs_gdf.Sampledate > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
||||
|
||||
obs_HgWater = obs_gdf.loc[obsMask, :].copy()
|
||||
# Adjust Units
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'NG/L', 'Samplevalue'] = obs_HgWater.Samplevalue
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'ng/L', 'Samplevalue'] = obs_HgWater.Samplevalue
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'UG/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'ug/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'MG/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'mg/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000000
|
||||
|
||||
# Drop where no result
|
||||
obs_HgWater = obs_HgWater.dropna(subset=['Samplevalue'])
|
||||
|
||||
# Save subset of columns as a shapefile
|
||||
obs_HgWater.loc[:, ['Samplevalue', 'Sampledate', 'RiverKM', 'geometry',
|
||||
'Contributor', 'Sitecode', 'Parameter']].to_file(
|
||||
'C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/BairdDB_HgWater_RevB.shp')
|
||||
|
||||
|
||||
|
||||
|
||||
#%% River Profile Plotting
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
|
||||
|
||||
|
|
|
|||
|
|
@ -40,198 +40,59 @@ river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFr
|
|||
river_centerline_merge_gpd2.iloc[0, 1] = 0
|
||||
river_centerline_merge_gpd2.iloc[0, 2] = 0
|
||||
|
||||
#%% Read in water depth to get wet and dry points
|
||||
|
||||
#%% Read in combined dataset
|
||||
# obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/combined dataset-SGJ.csv")
|
||||
# obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/combined dataset.csv")
|
||||
# obs_IN = pd.read_csv("C:/Users/arey/Downloads/ExportDownloads_EwrrpDataExport_rharris_2023-05-12_104911/EwrrpDataExport_rharris_2023-05-12_104911.txt", sep='\t', encoding='UTF-16 LE')
|
||||
df = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Results.txt", sep = '\t', encoding='UTF-16 LE')
|
||||
locations = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Sites.txt", sep = '\t', encoding='UTF-16 LE')
|
||||
samples = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Samples.txt", sep = '\t', encoding='UTF-16 LE')
|
||||
depth_xyz = pd.read_csv('//srv-ott3/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/04_Bathymetry/Water depth at pressure points - mesh2d_nFaces_ mean.csv',
|
||||
names=['x', 'y', 'z', 'depth'], header=1)
|
||||
|
||||
#%% Merge locations and results
|
||||
obs_IN = pd.merge(left=df, right=locations, left_on= 'Samplesiteid', right_on= 'Id', how = 'left') # 1096 locations w/Hg
|
||||
obs_IN = pd.merge(left=obs_IN, right=samples, left_on= 'Sampleid', right_on= 'Id', how = 'left') # 1096 locations w/Hg
|
||||
# Make geodataframe
|
||||
depth_xyz_gp = gp.GeoDataFrame(depth_xyz, geometry=gp.points_from_xy(depth_xyz.x, depth_xyz.y), crs="EPSG:32615")
|
||||
|
||||
# Merge with centerline to get river km
|
||||
depth_xyz_gp = gp.sjoin_nearest(river_centerline_merge_gpd2, depth_xyz_gp, how='right')
|
||||
|
||||
#%% Create synthetic Hg data following Reed's equation
|
||||
# These need to be multiplied by the total mass of dry matter in each cell to get the total Hg/m2 in each layer
|
||||
CstartHg = 10
|
||||
CfinalHg = 1
|
||||
kHg = 0.08
|
||||
|
||||
depth_xyz_gp['Hg'] = (CstartHg - CfinalHg) * np.exp(-kHg * depth_xyz_gp['RiverKM']/1000) + CfinalHg
|
||||
|
||||
CstartMeHg = 0.05
|
||||
CfinalMeHg = 0.005
|
||||
kMeHg = 0.08
|
||||
|
||||
depth_xyz_gp['MeHg'] = (CstartMeHg - CfinalMeHg) * np.exp(-kMeHg * depth_xyz_gp['RiverKM']/1000) + CfinalMeHg
|
||||
|
||||
|
||||
# Add Datetime
|
||||
obs_IN['Sampledate'] = pd.to_datetime(obs_IN.Sampledate)
|
||||
# Set points where depth is zero (no water) to 0
|
||||
depth_xyz_gp.loc[depth_xyz_gp['depth'] == 0, 'Hg'] = 0
|
||||
depth_xyz_gp.loc[depth_xyz_gp['depth'] == 0, 'MeHg'] = 0
|
||||
|
||||
|
||||
|
||||
#%% Process combined datsaset
|
||||
# Convert to geodataframe
|
||||
obs_gdf = gp.GeoDataFrame(obs_IN, geometry=gp.points_from_xy(
|
||||
obs_IN.loc[:, 'Longitude'], obs_IN.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
|
||||
# Add centerline and reset index
|
||||
obs_gdf = gp.sjoin_nearest(river_centerline_merge_gpd2, obs_gdf, how='right', max_distance=250).reset_index()
|
||||
|
||||
# Sediment Hg GeoDataFrame where media is Sediment
|
||||
obsMask = (((obs_gdf.Media == 'SED') | (obs_gdf.Media == 'SOIL')) &
|
||||
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
|
||||
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
(obs_gdf.Sampledate > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna() &
|
||||
((obs_gdf.TopDepth == 0) | (obs_gdf.TopDepth.isna())))
|
||||
# obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
||||
# ((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
|
||||
# | (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
# (obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
||||
|
||||
obs_HgSed = obs_gdf.loc[obsMask, :].copy()
|
||||
|
||||
# Adjust Units
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Samplevalue']
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Samplevalue']
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Samplevalue'] * 1000
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Samplevalue'] * 1000
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Samplevalue'] * 0.001
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Samplevalue'] * 0.001
|
||||
|
||||
|
||||
|
||||
# Save subset of columns as a shapefile
|
||||
obs_HgSed.loc[:, ['Sample_NG/G', 'Sampledate_x', 'RiverKM', 'Latitude', 'Longitude', 'geometry',
|
||||
'Surveyname','Sitecode','Parameter']].to_file(
|
||||
'C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/BairdDB_HgSed.shp')
|
||||
|
||||
|
||||
#%% Extract Hg data from surface water samples
|
||||
|
||||
# Water Hg GeoDataFrame where media is SurfaceWater (SW)
|
||||
obsMask = (((obs_gdf.Media == 'SW')) &
|
||||
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)-Total')
|
||||
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
(obs_gdf.Sampledate > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
||||
|
||||
obs_HgWater = obs_gdf.loc[obsMask, :].copy()
|
||||
# Adjust Units
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'NG/L', 'Samplevalue'] = obs_HgWater.Samplevalue
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'ng/L', 'Samplevalue'] = obs_HgWater.Samplevalue
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'UG/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'ug/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'MG/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'mg/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000000
|
||||
|
||||
# Drop where no result
|
||||
obs_HgWater = obs_HgWater.dropna(subset=['Samplevalue'])
|
||||
|
||||
# Save subset of columns as a shapefile
|
||||
obs_HgWater.loc[:, ['Samplevalue', 'Sampledate', 'RiverKM', 'geometry',
|
||||
'Contributor', 'Sitecode', 'Parameter']].to_file(
|
||||
'C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/BairdDB_HgWater_RevB.shp')
|
||||
|
||||
|
||||
|
||||
|
||||
#%% River Profile Plotting
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
|
||||
|
||||
axes.scatter(obs_HgSed.RiverKM, obs_HgSed.Samplevalue)
|
||||
axes.scatter(obs_HgSed.RiverKM, obs_HgSed.Samplevalue)
|
||||
axes.set_ylim(0, 20000)
|
||||
axes.set_xlim(0, 100000)
|
||||
axes.set_xlabel('Distance Along River [m]')
|
||||
axes.set_ylabel('Surface Sediment Hg [ng/g]')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Hg_SurfaceProfile.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Plot Vertical Profiles
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(7, 7))
|
||||
obs_HgSed.plot.scatter('RiverKM', 'TopDepth_x', 12, 'Sample_NG/G',
|
||||
ax=axes, vmax=20000, cmap='viridis')
|
||||
|
||||
axes.invert_yaxis()
|
||||
axes.set_xlabel('Distance Along River [m]')
|
||||
axes.set_ylabel('Sample Depth [cm]')
|
||||
axes.set_xlim([0, 100000])
|
||||
|
||||
# Get colorbar axis to set a legend
|
||||
cax = fig.get_axes()[1]
|
||||
#and we can modify it, i.e.:
|
||||
cax.set_ylabel('Sediment Hg (ng/g)')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Hg_Vertical.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Hg Map Plotting
|
||||
#%% Hg Test plot
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 7))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
# Full system limits
|
||||
# axes.set_xlim(350000, 520000)
|
||||
# axes.set_ylim(5510000, 5580000)
|
||||
|
||||
# # Clay Lake Limits
|
||||
# axes.set_xlim(466418, 515846)
|
||||
# axes.set_ylim(5513437, 5545362)
|
||||
|
||||
# Dryden Limits
|
||||
axes.set_xlim(497697, 515846)
|
||||
axes.set_ylim(5513437, 5525166)
|
||||
|
||||
|
||||
# Add Basemap
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
|
||||
# ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
|
||||
|
||||
# Plot Hg At surface
|
||||
obs_HgSed.plot(ax=axes, column='Samplevalue', legend=True,
|
||||
vmin=0, vmax=5000, markersize=2, cmap='viridis',
|
||||
legend_kwds={'label': 'Surface Sediment Hg (ng/g)'})
|
||||
# Plot river distance using geopandas
|
||||
# depth_xyz_gp.plot(ax=axes, column='RiverKM', cmap='viridis')
|
||||
depth_xyz_gp.loc[depth_xyz_gp['depth'] != 0, :].plot(ax=axes, column='Hg', cmap='viridis')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/SurfaceHgMap.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
#%% Save to csv
|
||||
np.savetxt('//srv-ott3/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/99_Setups/SynHg_ug_g.xyz',
|
||||
np.array([depth_xyz_gp.geometry.x.values, depth_xyz_gp.geometry.y.values, depth_xyz_gp['Hg'].values]).T,
|
||||
delimiter=" ")
|
||||
|
||||
|
||||
#%% Interpolate Hg
|
||||
bathy_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Bed_Level.xyz',
|
||||
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
||||
|
||||
rough_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Roughness.xyz',
|
||||
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
||||
|
||||
HgInterp = griddata(np.array([dataOUTmax.geometry.x, dataOUTmax.geometry.y]).T,
|
||||
np.array(dataOUTmax.Samplevalue).T, np.array([bathy_xyz.x, bathy_xyz.y]).T,
|
||||
method='nearest')
|
||||
|
||||
HgInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, HgInterp))
|
||||
HgInterpOut = np.transpose(HgInterpOut)
|
||||
|
||||
# Set Wetlands to zero
|
||||
# Interpolate Roughness
|
||||
RoughInterp = sp.interpolate.griddata(np.transpose(np.array([rough_xyz.x, rough_xyz.y])), rough_xyz.z, np.transpose(np.array([bathy_xyz.x, bathy_xyz.y])),
|
||||
method='nearest')
|
||||
|
||||
HgInterpOut[RoughInterp==0.05, 2] = 0
|
||||
|
||||
np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/SurfaceInterFiltDB.xyz',
|
||||
HgInterpOut, delimiter=" ")
|
||||
|
||||
|
||||
#%% Hg Interp Plotting
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 7))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
# Clay Lake Limits
|
||||
axes.set_xlim(466418, 515846)
|
||||
axes.set_ylim(5513437, 5545362)
|
||||
# Add Basemap
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
|
||||
# Plot Hg
|
||||
axes.scatter(HgInterpOut[:, 0], HgInterpOut[:, 1], 5, HgInterpOut[:, 2],
|
||||
vmin=0, vmax=5000)
|
||||
|
||||
plt.show()
|
||||
np.savetxt('//srv-ott3/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/99_Setups/SynMeHg_ug_g.xyz',
|
||||
np.array([depth_xyz_gp.geometry.x.values, depth_xyz_gp.geometry.y.values, depth_xyz_gp['MeHg'].values]).T,
|
||||
delimiter=" ")
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,33 @@
|
|||
#%% Create a Delft3D WAQ segment input file
|
||||
# Alexander Rey, 2023-06-27
|
||||
|
||||
# These files can be used to define a value for every cell at every time step
|
||||
# The files are binary and have a specific format
|
||||
# The format is described in the Delft3D WAQ manual
|
||||
# Each lauer is a different segmnent
|
||||
# Typically, segments increase sequentially from 1 to N, then the second layer will be N+1 to 2N, etc.
|
||||
# The number of layers is defined in the GUI
|
||||
# Use QUICKPLOT to extract segment numbers in layer 1, then adjust from there
|
||||
# Note: it will go to zero at the end of the time series, so ensure that the last time step is covered
|
||||
|
||||
#%% Import libraries
|
||||
import struct
|
||||
|
||||
NOSEG = 5 # Number of segments
|
||||
NOTIME = 10 # Number of time steps
|
||||
|
||||
VALUE = [[0.0] * NOSEG for _ in range(NOTIME)] # Initialize VALUE as a 2D matrix
|
||||
|
||||
for i in range(NOTIME):
|
||||
for j in range(NOSEG):
|
||||
# Fill the matrix with the desired values.
|
||||
VALUE[i][j] = i + j
|
||||
|
||||
with open('output_seg10.bin', 'wb') as file:
|
||||
for i in range(NOTIME):
|
||||
# TIMER is the number of seconds from the reference date (set in the GUI)
|
||||
TIMER = (i - 1) * 31536000
|
||||
file.write(struct.pack('i', TIMER)) # Write TIMER as a 4-byte integer
|
||||
|
||||
for j in range(NOSEG):
|
||||
file.write(struct.pack('f', VALUE[i][j])) # Write VALUE[i][j] as a 4-byte float
|
||||
|
|
@ -0,0 +1 @@
|
|||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,67 @@
|
|||
#%% Example Penobscot Plotting Script
|
||||
# Alexander Rey, May 18, 2023
|
||||
|
||||
#%% Import modules
|
||||
import xarray as xr
|
||||
import matplotlib.pyplot as plt
|
||||
import contextily as ctx
|
||||
import numpy as np
|
||||
import dfm_tools as dfmt
|
||||
import pandas as pd
|
||||
import pathlib as pl
|
||||
|
||||
#%% Read in Model Log
|
||||
pth = pl.Path("N:/Penobscot/Modelling/12345.678.W.AJMR.Rev0_PenobscotModelLog.xlsx")
|
||||
|
||||
runLog = pd.read_excel(pth.as_posix(), "ModelLog")
|
||||
|
||||
dataPath = "FlowFM_map.nc"
|
||||
|
||||
|
||||
#%% Read in Delft3D Data
|
||||
i = 3
|
||||
|
||||
# Get hist file path
|
||||
file_nc_hist = pl.Path(runLog['Run Location'][i],
|
||||
'FlowFM', 'output', 'FlowFM_his.nc')
|
||||
file_nc_hist = "N:/Penobscot/Modelling/Runs/04_SalinityRun.dsproj_data/FlowFM/output/FlowFM_his.nc"
|
||||
# Open hist file dataset
|
||||
data_xr = xr.open_mfdataset(file_nc_hist, preprocess=dfmt.preprocess_hisnc)
|
||||
|
||||
# Get stations
|
||||
stations_pd = data_xr['stations'].to_dataframe()
|
||||
|
||||
#%% Extract and plot water level
|
||||
|
||||
# Extract water level
|
||||
waterlevel_xr = data_xr['waterlevel'].sel(stations=stations_pd.index[1])
|
||||
|
||||
# Plot water level
|
||||
fig, ax = plt.subplots(figsize=(12, 6))
|
||||
waterlevel_xr.plot(ax=ax, label='Water Level')
|
||||
|
||||
plt.show()
|
||||
|
||||
#%% Extract and plot water level map
|
||||
file_nc_map = "N:/Penobscot/Modelling/Runs/04_SalinityRun.dsproj_data/FlowFM/output/FlowFM_map.nc"
|
||||
|
||||
# Read in map file
|
||||
d3dfm_DataArray = dfmt.open_partitioned_dataset(file_nc_map)
|
||||
|
||||
# Get Var info
|
||||
vars_pd = dfmt.get_ncvarproperties(d3dfm_DataArray)
|
||||
|
||||
# Get map of water level
|
||||
waterlevel_map_xr = d3dfm_DataArray.mesh2d_s1[3, :].compute()
|
||||
|
||||
|
||||
#%% Plot water level map using delft3d map plotting function and contextily
|
||||
fig, ax = plt.subplots(figsize=(12, 6))
|
||||
|
||||
pc = waterlevel_map_xr.ugrid.plot(ax=ax, edgecolors='face', cmap='jet')
|
||||
|
||||
# Define crs
|
||||
crs = 'EPSG:32619'
|
||||
ctx.add_basemap(ax=ax, source=ctx.providers.Esri.WorldImagery, crs=crs, attribution=False)
|
||||
|
||||
plt.show()
|
||||
|
|
@ -10,6 +10,8 @@ import math
|
|||
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.font_manager as fm
|
||||
import matplotlib.dates as mdates
|
||||
|
||||
import matplotlib as mpl
|
||||
|
||||
import cartopy.crs as ccrs
|
||||
|
|
@ -81,7 +83,8 @@ importPaths = ['//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Resear
|
|||
None,
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20230212 WQ Sampling/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20230212 WQ Sampling/Crane',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20230212 WQ Sampling/Caribee']
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20230212 WQ Sampling/Caribee',
|
||||
'//srv-ott3.baird.com/Projects/13711.101 Caribee (Indigo) Hotel, Barbados/03_Data/03_Field/20220613_EnvData']
|
||||
|
||||
siteNames = ['Great House',
|
||||
'Greensleeves',
|
||||
|
|
@ -121,6 +124,7 @@ siteNames = ['Great House',
|
|||
None,
|
||||
'Old Queens Fort',
|
||||
'Crane',
|
||||
'Caribee',
|
||||
'Caribee']
|
||||
|
||||
timeLabels= ['Before Construction',
|
||||
|
|
@ -161,7 +165,8 @@ timeLabels= ['Before Construction',
|
|||
None,
|
||||
'February',
|
||||
'February',
|
||||
'February']
|
||||
'February',
|
||||
'June 2022']
|
||||
|
||||
wave_bts_file = [
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
|
|
@ -202,33 +207,69 @@ wave_bts_file = [
|
|||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None]
|
||||
|
||||
# %% Read in site shapefile
|
||||
siteShp = gp.read_file('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/SitePolygons_Crane_Caribee.shp')
|
||||
siteShp.geometry = siteShp.geometry.to_crs("EPSG:32621")
|
||||
|
||||
for s in [34, 36, 37, 38]: #range(15, 27):
|
||||
# Switch to save figures/ sheets
|
||||
figureSave = True
|
||||
|
||||
for s in range(22, 40): #range(15, 27):
|
||||
|
||||
## Define master import path
|
||||
importPath = importPaths[s]
|
||||
siteName = siteNames[s]
|
||||
timeLabel = timeLabels[s]
|
||||
importFiles = os.listdir(importPath)
|
||||
|
||||
# Skip if no importPath
|
||||
if importPath is None:
|
||||
print('Skipping site ' + str(s + 1))
|
||||
continue
|
||||
|
||||
# Monitor progress
|
||||
print('Processing site ' + str(s+1) + ' of ' + str(len(importPaths)) + ' (' + siteName + '_' + timeLabel + ')')
|
||||
|
||||
# Initialize import variables
|
||||
OBS_File = None
|
||||
|
||||
GPS_File = None
|
||||
RBR_File = None
|
||||
JFE_File = None
|
||||
|
||||
for i in range(0, len(importFiles)):
|
||||
if '.csv' in importFiles[i] and 'Summary' not in importFiles[i] and 'export' not in importFiles[i]:
|
||||
if '_A.csv' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
JFE_File = importFiles[i]
|
||||
elif '.csv' in importFiles[i] and 'Summary' not in importFiles[i] and 'export' not in importFiles[i]:
|
||||
OBS_File = importFiles[i]
|
||||
elif '.csv' in importFiles[i] and 'export' in importFiles[i]:
|
||||
GPS_File = importFiles[i]
|
||||
GPS_Type = 'CSV'
|
||||
elif '.xlsx' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
RBR_File = importFiles[i]
|
||||
elif '.xls' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
GPS_File = importFiles[i]
|
||||
GPS_Type = 'xls'
|
||||
|
||||
#%% RBR Import Data
|
||||
if RBR_File is not None:
|
||||
RBR_Obs = pd.read_excel(importPath + '/' + RBR_File,
|
||||
sheet_name='Data', skiprows=0, header=1)
|
||||
RBR_Obs['DateTime'] = pd.to_datetime(RBR_Obs['Time'])
|
||||
RBR_Obs.set_index('DateTime', inplace=True)
|
||||
|
||||
#%% JFE Import Data
|
||||
if JFE_File is not None:
|
||||
JFE_Obs = pd.read_csv(importPath + '/' + JFE_File, skiprows=30)
|
||||
JFE_Obs['DateTime'] = pd.to_datetime(JFE_Obs['Date'])
|
||||
# Add seconds to time based on mod 60 of index
|
||||
JFE_Obs['DateTime'] = JFE_Obs['DateTime'] + pd.to_timedelta(JFE_Obs.index % 60, unit='s')
|
||||
JFE_Obs.set_index('DateTime', inplace=True)
|
||||
|
||||
|
||||
#%% Obs Import Data
|
||||
if OBS_File is not None:
|
||||
Obs_dat = pd.read_csv(importPath + '/' + OBS_File, skiprows=0, header=0)
|
||||
|
|
@ -245,6 +286,26 @@ for s in [34, 36, 37, 38]: #range(15, 27):
|
|||
# Set Index
|
||||
Obs_dat.set_index('DateTime', inplace=True)
|
||||
|
||||
else:
|
||||
# Merge RBR and JFS obs into one dataframe
|
||||
if JFE_File is not None:
|
||||
Obs_dat = pd.merge(RBR_Obs, JFE_Obs, how='outer', on='DateTime')
|
||||
else:
|
||||
Obs_dat = RBR_Obs
|
||||
|
||||
# Sort index by DateTime
|
||||
Obs_dat.sort_index(inplace=True)
|
||||
|
||||
# Rename columns to match Obs_dat
|
||||
Obs_dat = Obs_dat.rename(columns={'Pressure ': 'CH0:Pressure(dbar)', 'Temperature ': 'CH1:Temperature(degC)',
|
||||
'Chl-a[ug/l]': 'CH2:Chlorophyll_a(ppb)', 'Conductivity ': 'CH3:Conductivity(mS/cm)',
|
||||
'Temp.[deg C]': 'CH4:Temperature(degC)', 'Turb. -M[FTU]': 'CH6:Turbidity(NTU)',
|
||||
'Dissolved O₂ saturation ': 'CH8:Oxygen_Saturation(%)'})
|
||||
|
||||
# Set Time Zone for Sensor. Sometimes it is set to UTC, sometimes it is set to EST
|
||||
Obs_dat.index = Obs_dat.index.tz_localize('UTC')
|
||||
|
||||
|
||||
#%% GPS Import Data
|
||||
if GPS_File is not None:
|
||||
if GPS_Type == 'CSV':
|
||||
|
|
@ -267,22 +328,29 @@ for s in [34, 36, 37, 38]: #range(15, 27):
|
|||
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['time']).dt.tz_localize('UTC')
|
||||
|
||||
# Set Datetime as index
|
||||
GPS_gdf.set_index('DateTime', inplace=True)
|
||||
else:
|
||||
# Synthesize GPS data for great house
|
||||
GPS_gdf = gp.read_file('C:/Users/arey/files/Projects/West Coast/GreatHousePath_R3.shp')
|
||||
GPS_gdf['DateTime'] = pd.date_range(pd.to_datetime(RBR_Obs['Time'].iloc[0]),
|
||||
pd.to_datetime(RBR_Obs['Time'].iloc[-1]),
|
||||
periods=len(GPS_gdf)).values
|
||||
|
||||
# Sort by time
|
||||
GPS_gdf.sort_index(inplace=True)
|
||||
# Set Datetime as index
|
||||
GPS_gdf.set_index('DateTime', inplace=True)
|
||||
|
||||
# Convert to UTM
|
||||
GPS_gdf.geometry = GPS_gdf.geometry.to_crs("EPSG:32621")
|
||||
# Sort by time
|
||||
GPS_gdf.sort_index(inplace=True)
|
||||
|
||||
#%% Merge GPS to RBR
|
||||
# Process RBR into datetime
|
||||
if OBS_File is not None:
|
||||
# Merge with GPS as dataframe
|
||||
Obs_geo = pd.merge_asof(Obs_dat, GPS_gdf,
|
||||
left_index=True, right_index=True, direction='nearest', tolerance=pd.Timedelta('300s'))
|
||||
Obs_geo = gp.GeoDataFrame(Obs_geo, geometry=Obs_geo.geometry, crs="EPSG:32621")
|
||||
# Convert to UTM
|
||||
GPS_gdf.geometry = GPS_gdf.geometry.to_crs("EPSG:32621")
|
||||
|
||||
|
||||
#%% Merge Obs to RBR
|
||||
# Process Obs into datetime
|
||||
# Merge with GPS as dataframe
|
||||
Obs_geo = pd.merge_asof(Obs_dat, GPS_gdf,
|
||||
left_index=True, right_index=True, direction='nearest', tolerance=pd.Timedelta('300s'))
|
||||
Obs_geo = gp.GeoDataFrame(Obs_geo, geometry=Obs_geo.geometry, crs="EPSG:32621")
|
||||
|
||||
#%% Find and setup key plotting details
|
||||
# Shaded Water
|
||||
|
|
@ -322,7 +390,7 @@ for s in [34, 36, 37, 38]: #range(15, 27):
|
|||
GFS_Lat = 13.1075
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[4, 1])
|
||||
|
||||
# Set min and max times using conductivity
|
||||
# Set min and max times using shape
|
||||
if Obs_geo['inArea'].any():
|
||||
# First and last times from area in shapefile
|
||||
minTime = Obs_geo[Obs_geo['inArea']==True].index[0]
|
||||
|
|
@ -455,240 +523,319 @@ for s in [34, 36, 37, 38]: #range(15, 27):
|
|||
met_tp = -999
|
||||
met_mwd = -999
|
||||
|
||||
#%% Add PSU and depth units
|
||||
Obs_geo['PSU'] = gsw.conversions.SP_from_C(Obs_geo['CH3:Conductivity(mS/cm)'].values,
|
||||
Obs_geo['CH4:Temperature(degC)'].values,
|
||||
Obs_geo['CH0:Pressure(dbar)'].values)
|
||||
#%% Add PSU and depth units if not present
|
||||
|
||||
if ('Salinity ' not in Obs_geo.columns) and (JFE_File is None):
|
||||
Obs_geo['PSU'] = gsw.conversions.SP_from_C(Obs_geo['CH3:Conductivity(mS/cm)'].values,
|
||||
Obs_geo['CH4:Temperature(degC)'].values,
|
||||
Obs_geo['CH0:Pressure(dbar)'].values)
|
||||
|
||||
|
||||
Obs_geo['Depth'] = (Obs_geo['CH0:Pressure(dbar)'].astype(float)*10000)/(gsw.rho(Obs_geo['PSU'].values,
|
||||
Obs_geo['CH1:Temperature(degC)'].values,
|
||||
Obs_geo['CH0:Pressure(dbar)'].values) * 9.81)
|
||||
Obs_geo['Depth'] = (Obs_geo['CH0:Pressure(dbar)'].astype(float)*10000)/(gsw.rho(Obs_geo['PSU'].values,
|
||||
Obs_geo['CH1:Temperature(degC)'].values,
|
||||
Obs_geo['CH0:Pressure(dbar)'].values) * 9.81)
|
||||
elif (JFE_File is not None):
|
||||
# Rename Salinity to PSU
|
||||
Obs_geo.rename(columns={'Salinity ': 'PSU'}, inplace=True)
|
||||
# Fix depth name
|
||||
Obs_geo.rename(columns={'Depth ': 'Depth'}, inplace=True)
|
||||
|
||||
else: # Fix turbidity name
|
||||
Obs_geo.rename(columns={'Turbidity ': 'CH6:Turbidity(NTU)'}, inplace=True)
|
||||
|
||||
|
||||
#%% Save data as geojson
|
||||
Obs_geo.loc[minTime:maxTime, :].to_file(
|
||||
importPaths[s] + '/MergedGeoDataFrame.geojson', driver='GeoJSON')
|
||||
|
||||
|
||||
#%% Plot time series for Geo data
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
if figureSave:
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
|
||||
if (JFE_File is None) and (RBR_File is not None):
|
||||
fig, axesTMP = plt.subplots(nrows=1, ncols=1, figsize=(19, 5), constrained_layout=True)
|
||||
axes = []
|
||||
axes.append(axesTMP)
|
||||
|
||||
fig, axes = plt.subplots(nrows=6, ncols=1, figsize=(19, 25), constrained_layout=True)
|
||||
dataTable = np.zeros([14, 4])
|
||||
roundIDX = [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]
|
||||
dataTable = np.zeros([9, 4])
|
||||
roundIDX = [1, 1, 0, 1, 1, 1, 1, 1]
|
||||
|
||||
params = ['Depth', 'PSU', 'CH8:Oxygen_Saturation(%)', 'CH1:Temperature(degC)',
|
||||
'CH6:Turbidity(NTU)', 'CH2:Chlorophyll_a(ppb)']
|
||||
paramName = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ sat [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]']
|
||||
params = ['CH6:Turbidity(NTU)']
|
||||
paramName = ['Turbidity [FTU]']
|
||||
parmCmap = [cmo.turbid]
|
||||
RBRparamMin = [0.0]
|
||||
RBRparamMax = [60.0]
|
||||
paramMin = [0]
|
||||
paramMax = [60]
|
||||
order = 12
|
||||
smoothIDX = [60]
|
||||
smoothPeriod = '1 Minute Average'
|
||||
|
||||
parmCmap = [cmo.deep, 'cividis', cmo.dense, cmo.thermal, cmo.turbid, cmo.algae]
|
||||
# paramMin = [0.0, 34.0, 32.5, 25.0, 0, 0]
|
||||
# paramMax = [1.0, 36.0, 34.0, 31.0, 20.0, 1.0]
|
||||
paramMin = [0.0, 32.0, 100, 24.0, 0, 0]
|
||||
paramMax = [6, 36.5, 130, 34.0, 150.0, 12000]
|
||||
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
# fig.tight_layout(pad=1.05)
|
||||
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
|
||||
dataTable[0, 0] = met_temp.m.item(0)-272.15
|
||||
dataTable[1, 0] = met_wind
|
||||
dataTable[2, 0] = met_wdir
|
||||
dataTable[3, 0] = met_prep.m.item(0)
|
||||
dataTable[4, 0] = met_hmo
|
||||
dataTable[5, 0] = met_tp
|
||||
dataTable[6, 0] = met_mwd
|
||||
|
||||
ilocs_max = []
|
||||
ilocs_max_pts = []
|
||||
OBS_mask = []
|
||||
|
||||
for paramIDX, param in enumerate(params):
|
||||
Obs_geo.loc[minTime:maxTime, param].plot(
|
||||
ax=axes[paramIDX], label='1 Second Observations', color='lightgrey') # Note the space in the col name
|
||||
|
||||
# Create mask for RBR data based on time and parameter minimum
|
||||
OBS_mask.append(((Obs_geo.index>minTime) &
|
||||
(Obs_geo.index<maxTime) &
|
||||
(Obs_geo[param]>paramMin[paramIDX])))
|
||||
|
||||
OBS_smoothed = Obs_geo.loc[OBS_mask[paramIDX], param].rolling(
|
||||
12, win_type='nuttall',center=True).mean()
|
||||
|
||||
OBS_smoothed.plot(
|
||||
ax=axes[paramIDX], label='10 Second Average', color='black',
|
||||
linewidth=3)
|
||||
|
||||
# Find the local maximums for Turbidity
|
||||
if param == 'CH6:Turbidity(NTU)':
|
||||
##### Adjust "order" to control the number of turbidity points that are selected
|
||||
ilocs_max.append(argrelextrema(OBS_smoothed.values,
|
||||
np.greater_equal, order=12, mode='wrap')[0])
|
||||
|
||||
# Add start and end points?
|
||||
# ilocs_max = np.insert(ilocs_max, 0, 10)
|
||||
# ilocs_max[-1] = len(OBS_smoothed.values)
|
||||
|
||||
ilocs_max_pts.append(OBS_smoothed.iloc[ilocs_max[paramIDX]].index.values)
|
||||
|
||||
# Add labels if GPS data is available
|
||||
if GPS_File is not None:
|
||||
# axes[paramIDX].scatter(OBS_smoothed.iloc[
|
||||
# ilocs_max[paramIDX]].index, np.ones(len(ilocs_max[paramIDX])) * 30, 75,
|
||||
# color='blue')
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
axes[paramIDX].annotate(str(a+1), (ilocs_max_pts[paramIDX][a], 12), fontsize=30)
|
||||
else:
|
||||
ilocs_max.append(None)
|
||||
ilocs_max_pts.append(None)
|
||||
fig, axes = plt.subplots(nrows=6, ncols=1, figsize=(19, 25), constrained_layout=True)
|
||||
dataTable = np.zeros([14, 4])
|
||||
params = ['Depth', 'PSU', 'CH8:Oxygen_Saturation(%)', 'CH1:Temperature(degC)',
|
||||
'CH6:Turbidity(NTU)', 'CH2:Chlorophyll_a(ppb)']
|
||||
paramName = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ sat [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]']
|
||||
|
||||
dataTable[paramIDX+7, 0] = OBS_smoothed.mean(skipna=True)
|
||||
dataTable[paramIDX+7, 1] = OBS_smoothed.std(skipna=True)
|
||||
dataTable[paramIDX+7, 2] = max(OBS_smoothed.min(skipna=True), 0)
|
||||
dataTable[paramIDX+7, 3] = OBS_smoothed.max(skipna=True)
|
||||
parmCmap = [cmo.deep, 'cividis', cmo.dense, cmo.thermal, cmo.turbid, cmo.algae]
|
||||
roundIDX = [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]
|
||||
if s<12: # Original sensor
|
||||
# paramMin = [0.0, 34.0, 32.5, 25.0, 0, 0]
|
||||
# paramMax = [6.0, 36.0, 34.0, 31.0, 20.0, 1.0]
|
||||
paramMin = [0.0, 32.0, 32.5, 24.0, 0, 0]
|
||||
paramMax = [2.0, 36.0, 34.0, 34.0, 20.0, 1.0]
|
||||
order = 12
|
||||
smoothIDX = [60, 60, 60, 20, 20, 20]
|
||||
smoothPeriod = '1 Minute Average'
|
||||
else: # New sensor
|
||||
paramMin = [0.0, 32.0, 100, 24.0, 0, 0]
|
||||
paramMax = [6, 36.5, 130, 34.0, 150.0, 12000]
|
||||
order = 60
|
||||
smoothIDX = [12, 12, 12, 12, 12, 12]
|
||||
smoothPeriod = '10 Second Average'
|
||||
|
||||
axes[paramIDX].set_ylabel(paramName[paramIDX])
|
||||
axes[paramIDX].set_title(paramName[paramIDX])
|
||||
axes[paramIDX].set_xlabel('')
|
||||
axes[paramIDX].set_ylim(paramMin[paramIDX], paramMax[paramIDX])
|
||||
axes[paramIDX].legend(loc='upper right')
|
||||
|
||||
# axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))
|
||||
|
||||
# Formate Data Table
|
||||
dataTableFormat_mean = []
|
||||
dataTableFormat_maxmin = []
|
||||
for d in range(0, len(roundIDX)):
|
||||
dataTableFormat_mean.append(round(dataTable[d, 0], roundIDX[d]))
|
||||
if dataTable[d, 3] == 0:
|
||||
dataTableFormat_maxmin.append('--')
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
# fig.tight_layout(pad=1.05)
|
||||
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
|
||||
dataTable[0, 0] = met_temp.m.item(0)-272.15
|
||||
dataTable[1, 0] = met_wind
|
||||
dataTable[2, 0] = met_wdir
|
||||
dataTable[3, 0] = met_prep.m.item(0)
|
||||
dataTable[4, 0] = met_hmo
|
||||
dataTable[5, 0] = met_tp
|
||||
dataTable[6, 0] = met_mwd
|
||||
|
||||
ilocs_max = []
|
||||
ilocs_max_pts = []
|
||||
OBS_mask = []
|
||||
|
||||
for paramIDX, param in enumerate(params):
|
||||
Obs_geo.loc[minTime:maxTime, param].plot(
|
||||
ax=axes[paramIDX], label='1 Second Observations', color='lightgrey') # Note the space in the col name
|
||||
|
||||
# Create mask for RBR data based on time and parameter minimum
|
||||
OBS_mask.append(((Obs_geo.index>minTime) &
|
||||
(Obs_geo.index<maxTime) &
|
||||
(Obs_geo[param]>paramMin[paramIDX])))
|
||||
|
||||
OBS_smoothed = Obs_geo.loc[OBS_mask[paramIDX], param].rolling(
|
||||
12, win_type='nuttall',center=True).mean()
|
||||
|
||||
OBS_smoothed.plot(
|
||||
ax=axes[paramIDX], label=smoothPeriod, color='black',
|
||||
linewidth=3)
|
||||
|
||||
# Find the local maximums for Turbidity
|
||||
if param == 'CH6:Turbidity(NTU)':
|
||||
##### Adjust "order" to control the number of turbidity points that are selected
|
||||
|
||||
# Find an order that creates between 5-10 points
|
||||
while (len(argrelextrema(OBS_smoothed.values, np.greater_equal, order=order, mode='wrap')[0]) < 5) or\
|
||||
(len(argrelextrema(OBS_smoothed.values, np.greater_equal, order=order, mode='wrap')[0]) > 10) or\
|
||||
(order == 0):
|
||||
|
||||
if len(argrelextrema(OBS_smoothed.values, np.greater_equal, order=order, mode='wrap')[0]) < 5:
|
||||
order = order - 5
|
||||
elif len(argrelextrema(OBS_smoothed.values, np.greater_equal, order=order, mode='wrap')[0]) > 10:
|
||||
order = order + 6
|
||||
|
||||
if order == 0:
|
||||
order = 1
|
||||
|
||||
ilocs_max.append(argrelextrema(OBS_smoothed.values,
|
||||
np.greater_equal, order=order, mode='wrap')[0])
|
||||
|
||||
|
||||
# Add start and end points?
|
||||
# ilocs_max = np.insert(ilocs_max, 0, 10)
|
||||
# ilocs_max[-1] = len(OBS_smoothed.values)
|
||||
|
||||
ilocs_max_pts.append(OBS_smoothed.iloc[ilocs_max[paramIDX]].index.values)
|
||||
|
||||
# Add labels if GPS data is available
|
||||
if GPS_File is not None:
|
||||
# axes[paramIDX].scatter(OBS_smoothed.iloc[
|
||||
# ilocs_max[paramIDX]].index, np.ones(len(ilocs_max[paramIDX])) * 30, 75,
|
||||
# color='blue')
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
axes[paramIDX].annotate(str(a+1), (ilocs_max_pts[paramIDX][a], 12), fontsize=30)
|
||||
else:
|
||||
ilocs_max.append(None)
|
||||
ilocs_max_pts.append(None)
|
||||
|
||||
dataTable[paramIDX+7, 0] = OBS_smoothed.mean(skipna=True)
|
||||
dataTable[paramIDX+7, 1] = OBS_smoothed.std(skipna=True)
|
||||
dataTable[paramIDX+7, 2] = max(OBS_smoothed.min(skipna=True), 0)
|
||||
dataTable[paramIDX+7, 3] = OBS_smoothed.max(skipna=True)
|
||||
|
||||
axes[paramIDX].set_ylabel(paramName[paramIDX])
|
||||
axes[paramIDX].set_title(paramName[paramIDX])
|
||||
axes[paramIDX].set_xlabel('')
|
||||
axes[paramIDX].set_ylim(paramMin[paramIDX], paramMax[paramIDX])
|
||||
axes[paramIDX].legend(loc='upper right')
|
||||
|
||||
# axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))
|
||||
|
||||
# Formate Data Table
|
||||
dataTableFormat_mean = []
|
||||
dataTableFormat_maxmin = []
|
||||
for d in range(0, len(roundIDX)):
|
||||
dataTableFormat_mean.append(round(dataTable[d, 0], roundIDX[d]))
|
||||
if dataTable[d, 3] == 0:
|
||||
dataTableFormat_maxmin.append('--')
|
||||
else:
|
||||
dataTableFormat_maxmin.append(str(round(dataTable[d, 2], roundIDX[d])) + ' / ' + str(round(dataTable[d, 3], roundIDX[d])))
|
||||
|
||||
|
||||
dfOut = pd.DataFrame(dataTable[:, :])
|
||||
dfOutFormat = pd.DataFrame([dataTableFormat_mean, dataTableFormat_maxmin]).transpose()
|
||||
|
||||
# Change the column names
|
||||
dfOut.columns =['Mean', 'Standard Deviation', 'Min', 'Max']
|
||||
dfOutFormat.columns = [np.array([minTime.strftime('%B %#d, %Y'), minTime.strftime('%B %#d, %Y')]),
|
||||
np.array(['Mean', 'Min / Max'])]
|
||||
# Change the row indexes
|
||||
|
||||
if (JFE_File is None) and (RBR_File is not None):
|
||||
dfOut.iloc[8, 0] = minTime.strftime('%B %#d, %Y')
|
||||
rowNames = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
||||
'Significant Wave Height Offshore [m]', 'Peak Wave Period Offshore [s]',
|
||||
'Mean Wave Direction Offshore [deg]',
|
||||
'Turbidity [NTU]', 'Observation Date']
|
||||
dfOut.index = rowNames
|
||||
dfOutFormat.index = rowNames[0:-1]
|
||||
|
||||
else:
|
||||
dataTableFormat_maxmin.append(str(round(dataTable[d, 2], roundIDX[d])) + ' / ' + str(round(dataTable[d, 3], roundIDX[d])))
|
||||
dfOut.iloc[13, 0] = minTime.strftime('%B %#d, %Y')
|
||||
rowNames = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
||||
'Significant Wave Height Offshore [m]' ,'Peak Wave Period Offshore [s]',
|
||||
'Mean Wave Direction Offshore [deg]', 'Depth [m]', 'Salinity [PSU]',
|
||||
'Dissolved O₂ saturation [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]', 'Observation Date']
|
||||
|
||||
|
||||
dfOut = pd.DataFrame(dataTable[:, :])
|
||||
dfOutFormat = pd.DataFrame([dataTableFormat_mean, dataTableFormat_maxmin]).transpose()
|
||||
dfOut.index = rowNames
|
||||
dfOutFormat.index = rowNames[0:-1]
|
||||
|
||||
# Change the column names
|
||||
dfOut.columns =['Mean', 'Standard Deviation', 'Min', 'Max']
|
||||
dfOutFormat.columns = [np.array([minTime.strftime('%B %#d, %Y'), minTime.strftime('%B %#d, %Y')]),
|
||||
np.array(['Mean', 'Min / Max'])]
|
||||
# Change the row indexes
|
||||
dfOut.iloc[13, 0] = minTime.strftime('%B %#d, %Y')
|
||||
rowNames = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
||||
'Significant Wave Height Offshore [m]' ,'Peak Wave Period Offshore [s]',
|
||||
'Mean Wave Direction Offshore [deg]', 'Depth [m]', 'Salinity [PSU]',
|
||||
'Dissolved O₂ saturation [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]', 'Observation Date']
|
||||
fig.suptitle(siteName + ', ' + minTime.strftime('%B %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
|
||||
plt.show()
|
||||
|
||||
dfOut.index = rowNames
|
||||
dfOutFormat.index = rowNames[0:-1]
|
||||
# outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
||||
outPath = importPaths[s]
|
||||
|
||||
fig.suptitle(siteName + ', ' + minTime.strftime('%B %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
if not os.path.exists(outPath):
|
||||
os.mkdir(outPath)
|
||||
|
||||
plt.show()
|
||||
dfOut.to_excel(outPath + '/Summary_Stats_' + siteName + '_RevB.xlsx')
|
||||
dfOutFormat.to_excel(outPath + '/Summary_StatsFormat_' + siteName + '_RevB.xlsx')
|
||||
|
||||
# outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
||||
outPath = importPaths[s]
|
||||
dfOut.to_csv(outPath + '/Summary_Stats_' + siteName + '_RevB.csv')
|
||||
|
||||
if not os.path.exists(outPath):
|
||||
os.mkdir(outPath)
|
||||
if not os.path.exists(outPath + '/Figures'):
|
||||
os.mkdir(outPath + '/Figures')
|
||||
|
||||
dfOut.to_excel(outPath + '/Summary_Stats_' + siteName + '.xlsx')
|
||||
dfOutFormat.to_excel(outPath + '/Summary_StatsFormat_' + siteName + '.xlsx')
|
||||
|
||||
dfOut.to_csv(outPath + '/Summary_Stats_' + siteName + '.csv')
|
||||
|
||||
if not os.path.exists(outPath + '/Figures'):
|
||||
os.mkdir(outPath + '/Figures')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '_RevB.pdf',
|
||||
bbox_inches='tight')
|
||||
fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '_RevB.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
|
||||
|
||||
#%% Plot Maps
|
||||
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(19, 25), constrained_layout=True)
|
||||
ax = axes.flat
|
||||
if figureSave:
|
||||
if (JFE_File is None) and (RBR_File is not None):
|
||||
# Only Turbidity Data
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(9, 9), constrained_layout=True)
|
||||
ax = []
|
||||
ax.append(axes)
|
||||
else:
|
||||
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(19, 25), constrained_layout=True)
|
||||
ax = axes.flat
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
# fig.tight_layout(pad=1.05)
|
||||
fig.patch.set_facecolor('white')
|
||||
# fig.tight_layout(pad=1.05)
|
||||
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
x, y, arrow_length = 0.95, 0.93, 0.20
|
||||
plt.rcParams.update({'font.size': 22})
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
x, y, arrow_length = 0.95, 0.93, 0.20
|
||||
plt.rcParams.update({'font.size': 22})
|
||||
|
||||
axXlimTT = (Obs_geo.loc[minTime:maxTime].geometry.x.min()-100,
|
||||
Obs_geo.loc[minTime:maxTime].geometry.x.max()+100)
|
||||
axYlimTT = (Obs_geo.loc[minTime:maxTime].geometry.y.min()-100,
|
||||
Obs_geo.loc[minTime:maxTime].geometry.y.max()+100)
|
||||
axXlimTT = (Obs_geo.loc[minTime:maxTime].geometry.x.min()-100,
|
||||
Obs_geo.loc[minTime:maxTime].geometry.x.max()+100)
|
||||
axYlimTT = (Obs_geo.loc[minTime:maxTime].geometry.y.min()-100,
|
||||
Obs_geo.loc[minTime:maxTime].geometry.y.max()+100)
|
||||
|
||||
plt.setp(axes, xlim=axXlim, ylim=axYlim)
|
||||
# plt.setp(axes, xlim=axXlimTT, ylim=axYlimTT)
|
||||
plt.setp(axes, xlim=axXlim, ylim=axYlim)
|
||||
# plt.setp(axes, xlim=axXlimTT, ylim=axYlimTT)
|
||||
|
||||
# Plot the RBR observations
|
||||
# Salinity
|
||||
for paramIDX, param in enumerate(params):
|
||||
Obs_geo.loc[minTime:maxTime].plot(
|
||||
column=param, ax=ax[paramIDX], vmin=paramMin[paramIDX], vmax=paramMax[paramIDX],
|
||||
legend=True, legend_kwds={'label': paramName[paramIDX]},
|
||||
cmap=parmCmap[paramIDX], markersize=20)
|
||||
# Plot the RBR observations
|
||||
# Salinity
|
||||
for paramIDX, param in enumerate(params):
|
||||
Obs_geo.loc[minTime:maxTime].plot(
|
||||
column=param, ax=ax[paramIDX], vmin=paramMin[paramIDX], vmax=paramMax[paramIDX],
|
||||
legend=True, legend_kwds={'label': paramName[paramIDX]},
|
||||
cmap=parmCmap[paramIDX], markersize=20)
|
||||
|
||||
|
||||
ctx.add_basemap(ax[paramIDX], source=mapbox, crs='EPSG:32621')
|
||||
ctx.add_basemap(ax[paramIDX], source=mapbox, crs='EPSG:32621')
|
||||
|
||||
# Add time labels
|
||||
# plt.scatter(RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||
# ilocs_max, :].geometry.x,
|
||||
# RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||
# ilocs_max, :].geometry.y, 75, marker='o', color='black')
|
||||
# Add time labels
|
||||
# plt.scatter(RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||
# ilocs_max, :].geometry.x,
|
||||
# RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||
# ilocs_max, :].geometry.y, 75, marker='o', color='black')
|
||||
|
||||
if (not Obs_geo.geometry.isnull().all()) &\
|
||||
(GPS_File is not None) & (param == 'CH6:Turbidity(NTU)'):
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
ax[paramIDX].annotate(str(a + 1), (Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.x,
|
||||
Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.y), fontsize=30)
|
||||
if (not Obs_geo.geometry.isnull().all()) &\
|
||||
(GPS_File is not None) & (param == 'CH6:Turbidity(NTU)'):
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
ax[paramIDX].annotate(str(a + 1), (Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.x,
|
||||
Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.y), fontsize=30)
|
||||
|
||||
ax[paramIDX].set_title(paramName[paramIDX])
|
||||
# ax[paramIDX].set_ylabel('UTM 21N [m]')
|
||||
# ax[paramIDX].set_xlabel('UTM 21N [m]')
|
||||
ax[paramIDX].locator_params(axis='y', nbins=3)
|
||||
ax[paramIDX].ticklabel_format(useOffset=False, style='plain', axis='both')
|
||||
ax[paramIDX].set_title(paramName[paramIDX])
|
||||
# ax[paramIDX].set_ylabel('UTM 21N [m]')
|
||||
# ax[paramIDX].set_xlabel('UTM 21N [m]')
|
||||
ax[paramIDX].locator_params(axis='y', nbins=3)
|
||||
ax[paramIDX].ticklabel_format(useOffset=False, style='plain', axis='both')
|
||||
|
||||
ax[paramIDX].get_xaxis().set_ticks([])
|
||||
ax[paramIDX].get_yaxis().set_ticks([])
|
||||
ax[paramIDX].get_xaxis().set_ticks([])
|
||||
ax[paramIDX].get_yaxis().set_ticks([])
|
||||
|
||||
#Add scale-bar
|
||||
scalebar = AnchoredSizeBar(ax[paramIDX].transData,
|
||||
100, '100 m', 'lower right', pad=0.5, size_vertical=10, fontproperties=fontprops)
|
||||
ax[paramIDX].add_artist(scalebar)
|
||||
ax[paramIDX].annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
|
||||
arrowprops=dict(facecolor='black', width=6, headwidth=30),
|
||||
ha='center', va='center', fontsize=35,
|
||||
xycoords=ax[paramIDX].transAxes)
|
||||
#Add scale-bar
|
||||
scalebar = AnchoredSizeBar(ax[paramIDX].transData,
|
||||
100, '100 m', 'lower right', pad=0.5, size_vertical=10, fontproperties=fontprops)
|
||||
ax[paramIDX].add_artist(scalebar)
|
||||
ax[paramIDX].annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
|
||||
arrowprops=dict(facecolor='black', width=6, headwidth=30),
|
||||
ha='center', va='center', fontsize=35,
|
||||
xycoords=ax[paramIDX].transAxes)
|
||||
|
||||
fig.suptitle(siteName + ', ' + minTime.strftime('%b %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
fig.suptitle(siteName + ', ' + minTime.strftime('%b %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
|
||||
plt.show()
|
||||
plt.show()
|
||||
|
||||
#outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
||||
outPath = importPaths[s]
|
||||
#outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
||||
outPath = importPaths[s]
|
||||
|
||||
if not os.path.exists(outPath):
|
||||
os.mkdir(outPath)
|
||||
if not os.path.exists(outPath):
|
||||
os.mkdir(outPath)
|
||||
|
||||
if not os.path.exists(outPath + '/Figures'):
|
||||
os.mkdir(outPath + '/Figures')
|
||||
if not os.path.exists(outPath + '/Figures'):
|
||||
os.mkdir(outPath + '/Figures')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabels[s] + '_' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabels[s] + '_' + siteName + '_RevB.pdf',
|
||||
bbox_inches='tight')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabels[s] + '_' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabels[s] + '_' + siteName + '_RevB.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
|
||||
#%% Summary Sheet
|
||||
plotIDXsLoop = []
|
||||
|
|
@ -788,3 +935,150 @@ for i in range(2, 3):
|
|||
bbox_inches='tight')
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
|
||||
#%% Save tracks for one location to shapefile
|
||||
|
||||
mergedLines = []
|
||||
|
||||
# Loop through all samples
|
||||
for s in range(0, len(siteNames)):
|
||||
siteName = siteNames[s]
|
||||
|
||||
if siteName == 'Crane':
|
||||
importPath = importPaths[s]
|
||||
|
||||
# Load in track data
|
||||
locationGDF = gp.read_file(importPath + '/MergedGeoDataFrame.geojson')
|
||||
|
||||
# Convert points to linestring and add to list
|
||||
mergedLines.append(LineString([[a.x, a.y] for a in locationGDF.geometry.values]))
|
||||
|
||||
# Track progress
|
||||
print(s)
|
||||
|
||||
# Convert list of tracks to geodataframe
|
||||
mergedLines_gdf = gp.GeoDataFrame(geometry=mergedLines, crs="EPSG:32621")
|
||||
|
||||
# Save to shapefile
|
||||
mergedLines_gdf.to_file('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/Crane_Tracks.shp')
|
||||
|
||||
|
||||
|
||||
#%% Read GeoJSON file for each site and extract data at two points
|
||||
|
||||
# Plot trubidity at each point through time
|
||||
fig, axes = plt.subplots(1, 2, figsize=(15, 6))
|
||||
|
||||
# Define points of interest as geodataframe 5 m from point
|
||||
|
||||
meanTurbA = []
|
||||
meanTurbB = []
|
||||
meanTime = []
|
||||
|
||||
# Set plotting site
|
||||
# plotSite = 'Great House'
|
||||
# plotSite = 'Caribee'
|
||||
# plotSite = 'Greensleeves'
|
||||
# plotSite = 'Old Queens Fort'
|
||||
plotSite = 'Crane'
|
||||
|
||||
|
||||
# Mapping parameter
|
||||
if plotSite == 'Great House':
|
||||
axXlim = (213210.7529575412, 213562.64172686986)
|
||||
axYlim = (1464769.2243017585, 1465135.2219089477)
|
||||
A_pt = [213316.46, 1464972.00]
|
||||
B_pt = [213329.09, 1464898.30]
|
||||
|
||||
elif plotSite == 'Greensleeves':
|
||||
axXlim = (213269.99233348924, 213648.1643157148)
|
||||
axYlim = (1463378.1020314451, 1463950.5442048472)
|
||||
A_pt = [213391.92, 1463659.98]
|
||||
B_pt = [213518.95, 1463517.72]
|
||||
|
||||
elif plotSite == 'Old Queens Fort':
|
||||
axXlim = (213368.59866770002, 213745.6997016811)
|
||||
axYlim = (1460192.707288096, 1460672.371780407)
|
||||
A_pt = [213527.75, 1460354.13]
|
||||
B_pt = [213510.13, 1460565.55]
|
||||
|
||||
elif plotSite == 'Crane':
|
||||
axXlim = (234835, 235507)
|
||||
axYlim = (1449966, 1450580)
|
||||
A_pt = [235287.01, 1450171.33]
|
||||
B_pt = [235300.81, 1450233.21]
|
||||
|
||||
elif plotSite == 'Caribee':
|
||||
axXlim = (217929, 218345)
|
||||
axYlim = (1446528, 1446818)
|
||||
A_pt = [218120.54, 1446683.057]
|
||||
B_pt = [218250.99, 1446676.20]
|
||||
|
||||
# Buffer to 20 m
|
||||
A_GDF = gp.GeoDataFrame(geometry=gp.points_from_xy([A_pt[0]], [A_pt[1]]), crs="EPSG:32621").buffer(22)
|
||||
B_GDF = gp.GeoDataFrame(geometry=gp.points_from_xy([B_pt[0]], [B_pt[1]]), crs="EPSG:32621").buffer(22)
|
||||
|
||||
# Set Map Bounds
|
||||
axes[0].set_xlim(axXlim)
|
||||
axes[0].set_ylim(axYlim)
|
||||
|
||||
# Add Base Map
|
||||
ctx.add_basemap(axes[0], source=mapbox, crs='EPSG:32621')
|
||||
|
||||
# Loop through all samples
|
||||
for s in range(0, len(siteNames)):
|
||||
|
||||
siteName = siteNames[s]
|
||||
|
||||
if siteName == plotSite:
|
||||
importPath = importPaths[s]
|
||||
|
||||
# Load in track data
|
||||
locationGDF = gp.read_file(importPath + '/MergedGeoDataFrame.geojson')
|
||||
|
||||
# Find points within the point of interest buffer
|
||||
# Average turbidity
|
||||
meanTurbA.append(locationGDF.loc[locationGDF.within(A_GDF.iloc[0]), 'CH6:Turbidity(NTU)'].mean())
|
||||
meanTurbB.append(locationGDF.loc[locationGDF.within(B_GDF.iloc[0]), 'CH6:Turbidity(NTU)'].mean())
|
||||
meanTime.append(locationGDF.loc[0, 'DateTime'])
|
||||
|
||||
|
||||
# Plot Track
|
||||
locationGDF.plot(ax=axes[0])
|
||||
|
||||
# Plot turbidity locations as empty circles
|
||||
A_GDF.plot(ax=axes[0], facecolor="none", edgecolor='k', linewidth=5)
|
||||
B_GDF.plot(ax=axes[0], facecolor="none", edgecolor='k', linewidth=5)
|
||||
|
||||
# Add text labels to turbidity locations
|
||||
axes[0].text(A_pt[0]+30, A_pt[1]-10, 'A', fontsize=25)
|
||||
axes[0].text(B_pt[0]+30, B_pt[1]-10, 'B', fontsize=25)
|
||||
|
||||
# Remove ticks
|
||||
axes[0].get_xaxis().set_ticks([])
|
||||
axes[0].get_yaxis().set_ticks([])
|
||||
|
||||
# Plot turbidity
|
||||
axes[1].plot(pd.to_datetime(meanTime), meanTurbA, label='Turbidity Point A')
|
||||
axes[1].plot(pd.to_datetime(meanTime), meanTurbB, label='Turbidity Point B')
|
||||
|
||||
# Add Legend
|
||||
axes[1].legend()
|
||||
|
||||
# Add axis labels
|
||||
axes[1].set_xlabel('Date')
|
||||
axes[1].set_ylabel('Turbidity (NTU)')
|
||||
|
||||
# Limit x axis to 4 ticks
|
||||
axes[1].xaxis.set_major_locator(plt.MaxNLocator(4))
|
||||
|
||||
# Format x axis dates
|
||||
axes[1].xaxis.set_major_formatter(mdates.DateFormatter('%b %y'))
|
||||
|
||||
# Add plot title
|
||||
plt.suptitle(plotSite)
|
||||
|
||||
plt.show()
|
||||
|
||||
# Save figure
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + plotSite + '_Turbidity_Time.png', dpi=300, bbox_inches='tight')
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
#%% Scratch file to compare Delft3D input bounds
|
||||
# Alexander Rey, Nay 16, 2023
|
||||
|
||||
#%% Import modules
|
||||
import os
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
plt.close('all')
|
||||
import dfm_tools as dfmt
|
||||
import hydrolib.core.dflowfm as hcdfm
|
||||
import pandas as pd
|
||||
import datetime
|
||||
from matplotlib import dates as mpl_dates
|
||||
|
||||
#%% Read in Salinity data
|
||||
file_bc = r'\\ott-diomedes\Projects\11934.201_Newtown_Creek_TPP\Delft3D\Run169_2014HD_Sed_OrthoB_pliz_WorkingNoteNoMin_Wind.dsproj_data\FlowFM\input\Salinity.bc'
|
||||
|
||||
#Load .bc-file using HydroLib object ForcingModel.
|
||||
m = hcdfm.ForcingModel(file_bc)
|
||||
ForcingModel_object_out = hcdfm.ForcingModel()
|
||||
|
||||
# Create xarray object for SouthBound
|
||||
salinityN_xr = dfmt.forcinglike_to_Dataset(m.forcing[0], convertnan=True)
|
||||
salinityS_xr = dfmt.forcinglike_to_Dataset(m.forcing[2], convertnan=True)
|
||||
|
||||
#%% Read in Temperature data
|
||||
file_bc = r'\\ott-diomedes\Projects\11934.201_Newtown_Creek_TPP\Delft3D\Run169_2014HD_Sed_OrthoB_pliz_WorkingNoteNoMin_Wind.dsproj_data\FlowFM\input\Temperature.bc'
|
||||
|
||||
#Load .bc-file using HydroLib object ForcingModel.
|
||||
m = hcdfm.ForcingModel(file_bc)
|
||||
ForcingModel_object_out = hcdfm.ForcingModel()
|
||||
|
||||
# Create xarray object for NorthBound and SouthBound
|
||||
temperatureN_xr = dfmt.forcinglike_to_Dataset(m.forcing[0], convertnan=True)
|
||||
temperatureS_xr = dfmt.forcinglike_to_Dataset(m.forcing[2], convertnan=True)
|
||||
|
||||
|
||||
#%% Read in Water Level data
|
||||
file_bc = r'\\ott-diomedes\Projects\11934.201_Newtown_Creek_TPP\Delft3D\Run169_2014HD_Sed_OrthoB_pliz_WorkingNoteNoMin_Wind.dsproj_data\FlowFM\input\WaterLevel.bc'
|
||||
|
||||
#Load .bc-file using HydroLib object ForcingModel.
|
||||
m = hcdfm.ForcingModel(file_bc)
|
||||
ForcingModel_object_out = hcdfm.ForcingModel()
|
||||
|
||||
# Create xarray object for NorthBound
|
||||
waterlevelN_xr = dfmt.forcinglike_to_Dataset(m.forcing[0], convertnan=True)
|
||||
waterlevelS_xr = dfmt.forcinglike_to_Dataset(m.forcing[2], convertnan=True)
|
||||
|
||||
|
||||
#%% Plot Bound Data
|
||||
|
||||
# Set Time Bounds
|
||||
dateMin = pd.to_datetime(datetime.datetime(2015, 7, 18, 0, 0, 0, 0))
|
||||
dateMax = pd.to_datetime(datetime.datetime(2015, 7, 22, 0, 0, 0, 0))
|
||||
|
||||
fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(12, 6), sharex=True)
|
||||
|
||||
# Plot a line for salinity for all times for midpoint layer
|
||||
salinityN_xr.salinitybnd.isel(depth=9).plot.line(ax=ax[0], linestyle='--', color='r', label='North Top')
|
||||
salinityS_xr.salinitybnd.isel(depth=9).plot.line(ax=ax[0], linestyle='-', color='r', label='South Top')
|
||||
salinityN_xr.salinitybnd.isel(depth=0).plot.line(ax=ax[0], linestyle='--', color='b', label='North Bottom')
|
||||
salinityS_xr.salinitybnd.isel(depth=0).plot.line(ax=ax[0], linestyle='-', color='b', label='South Bottom')
|
||||
|
||||
temperatureN_xr.temperaturebnd.isel(depth=9).plot.line(ax=ax[1], linestyle='--', color='r')
|
||||
temperatureS_xr.temperaturebnd.isel(depth=9).plot.line(ax=ax[1], linestyle='-', color='r')
|
||||
temperatureN_xr.temperaturebnd.isel(depth=0).plot.line(ax=ax[1], linestyle='--', color='b')
|
||||
temperatureS_xr.temperaturebnd.isel(depth=0).plot.line(ax=ax[1], linestyle='-', color='b')
|
||||
|
||||
waterlevelN_xr.waterlevelbnd.plot.line(ax=ax[2], linestyle='--', color='r')
|
||||
waterlevelS_xr.waterlevelbnd.plot.line(ax=ax[2], linestyle='-', color='r')
|
||||
|
||||
# Set y axis limits
|
||||
ax[0].set_ylim(12, 27)
|
||||
ax[1].set_ylim(15, 25)
|
||||
ax[2].set_ylim(-2, 2)
|
||||
|
||||
ax[2].set_xlim(dateMin, dateMax)
|
||||
ax[2].xaxis.set_major_formatter(
|
||||
mpl_dates.ConciseDateFormatter(ax[2].xaxis.get_major_locator()))
|
||||
|
||||
ax[0].legend()
|
||||
|
||||
plt.show()
|
||||
|
|
@ -97,7 +97,7 @@ for i in modelPlot:
|
|||
Delft_WL[i] = data_xr.waterlevel.sel(stations=['West_WL', 'East_WL']).to_pandas()
|
||||
|
||||
# Put in UTC if needed
|
||||
if i == 104:
|
||||
if i == 104 or i == 105 or i >= 135:
|
||||
Delft_WL[i] = Delft_WL[i].tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -20,8 +20,18 @@ import matplotlib.animation as animation
|
|||
|
||||
import dfm_tools as dfmt
|
||||
|
||||
# Geotiff
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
import pathlib as pl
|
||||
|
||||
from shapely.geometry import Point, MultiPoint
|
||||
import alphashape
|
||||
|
||||
import rioxarray
|
||||
import xarray as xr
|
||||
|
||||
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
from dfm_tools.regulargrid import scatter_to_regulargrid
|
||||
|
|
@ -41,13 +51,14 @@ histPath = "FlowFM/dflowfm/output/FlowFM_his.nc"
|
|||
#%% Import Model results for static images
|
||||
|
||||
# Define which models to import
|
||||
modelPlot = [120, 121, 129, 130, 131, 132, 133, 134]
|
||||
modelPlot = [144]
|
||||
|
||||
d3dfm_DataArray = [None] * (max(modelPlot)+1)
|
||||
sedMass = [None] * (max(modelPlot)+1) # Available Mass of Sediment
|
||||
erodeSed = [None] * (max(modelPlot)+1) # Erosion and deposition
|
||||
initialBed = [None] * (max(modelPlot)+1) # Erosion and deposition
|
||||
modelCRS = [None] * (max(modelPlot)+1) # Model CRS
|
||||
sedConc = [None] * (max(modelPlot)+1) # Model CRS
|
||||
|
||||
for i in modelPlot:
|
||||
file_nc_map = Path(runLog['Run Location'][i]) / dataPath
|
||||
|
|
@ -70,6 +81,9 @@ for i in modelPlot:
|
|||
erodeSed[i] = erodeSed[i].compute()
|
||||
initialBed[i] = initialBed[i].compute()
|
||||
|
||||
# Import concentration of sediment 2 at timestep 95 and layer 10
|
||||
sedConc[i] = d3dfm_DataArray[i].mesh2d_sedfrac_concentration[94, 2, :, 9].compute()
|
||||
|
||||
modelCRS[i] = vars_pd['EPSG_code']['projected_coordinate_system']
|
||||
|
||||
#%% Define axis limits
|
||||
|
|
@ -99,7 +113,7 @@ axLim_Xmin.append(305500)
|
|||
axLim_Xmax.append(306800)
|
||||
|
||||
axLim_Ymin.append(60000)
|
||||
axLim_Ymax.append(62200)
|
||||
axLim_Ymax.append(62500)
|
||||
|
||||
# Zoom far trib
|
||||
axLim_Xmin.append(305800)
|
||||
|
|
@ -161,8 +175,8 @@ for i in modelPlot:
|
|||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/ErodeDep_' + runLog['Run'][i] + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/InitialBed_' + runLog['Run'][i] + '.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/InitialBed_' + runLog['Run'][i] + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Plot using DFM functions
|
||||
#Cmap Path
|
||||
|
|
@ -425,3 +439,26 @@ for m in modelPlot:
|
|||
# Save video
|
||||
writer.finish()
|
||||
|
||||
|
||||
|
||||
#%% Save Sediment Geotiff
|
||||
|
||||
# Create mask within bounds
|
||||
boundSelect = 2
|
||||
croppedGridMask = (d3dfm_DataArray[i].mesh2d_face_x >= axLim_Xmin[boundSelect]) &\
|
||||
(d3dfm_DataArray[i].mesh2d_face_x <= axLim_Xmax[boundSelect]) &\
|
||||
(d3dfm_DataArray[i].mesh2d_face_y >= axLim_Ymin[boundSelect]) &\
|
||||
(d3dfm_DataArray[i].mesh2d_face_y <= axLim_Ymax[boundSelect])
|
||||
croppedGridMask = croppedGridMask.compute()
|
||||
|
||||
# Step through time steps
|
||||
for sedTstep in range(105, 120):
|
||||
# Rasterize sediment concentration within bounds
|
||||
sedConcTif = dfmt.rasterize_ugrid(d3dfm_DataArray[i]['mesh2d_sedfrac_concentration'].isel(
|
||||
time=sedTstep, mesh2d_nLayers=4, nSedSus=0).where(croppedGridMask, drop=True).to_dataset(),
|
||||
resolution=2)
|
||||
|
||||
# Add CRS
|
||||
sedConcTif.rio.write_crs("epsg:32118", inplace=True)
|
||||
sedConcTif.rio.to_raster('C:/Users/arey/files/Projects/Newtown/SedFigs2/GeoTiff_RevB_'
|
||||
+ 'Sed_Conc4_Time_' + str(sedTstep) + '.tif')
|
||||
|
|
|
|||
|
|
@ -41,6 +41,8 @@ from pathlib import Path
|
|||
# Colormaps
|
||||
import cm_xml_to_matplotlib as cm
|
||||
|
||||
import pytz
|
||||
|
||||
#%% Read Model Log
|
||||
|
||||
runLog = pd.read_excel('C:/Users/arey/files/Projects/Newtown/Model Log NTC.xlsx', 'ModelLog')
|
||||
|
|
@ -48,303 +50,172 @@ runLog = pd.read_excel('C:/Users/arey/files/Projects/Newtown/Model Log NTC.xlsx'
|
|||
dataPath = "FlowFM/dflowfm/output/FlowFM_map.nc"
|
||||
histPath = "FlowFM/dflowfm/output/FlowFM_his.nc"
|
||||
|
||||
#%% Import Model results for static images
|
||||
|
||||
# Define which models to import
|
||||
modelPlot = [144]
|
||||
|
||||
d3dfm_DataArray = [None] * (max(modelPlot)+1)
|
||||
sedMass = [None] * (max(modelPlot)+1) # Available Mass of Sediment
|
||||
erodeSed = [None] * (max(modelPlot)+1) # Erosion and deposition
|
||||
initialBed = [None] * (max(modelPlot)+1) # Erosion and deposition
|
||||
modelCRS = [None] * (max(modelPlot)+1) # Model CRS
|
||||
sedConc = [None] * (max(modelPlot)+1) # Model CRS
|
||||
|
||||
for i in modelPlot:
|
||||
file_nc_map = Path(runLog['Run Location'][i]) / dataPath
|
||||
|
||||
# Open Map file as xarrray Dataset
|
||||
d3dfm_DataArray[i] = dfmt.open_partitioned_dataset(file_nc_map.as_posix())
|
||||
|
||||
# Get Var info
|
||||
vars_pd = dfmt.get_ncvarproperties(d3dfm_DataArray[i])
|
||||
|
||||
# Get last timestep
|
||||
tStep = d3dfm_DataArray[i].time[-1].values
|
||||
|
||||
# Import sand sediment class
|
||||
sedMass[i] = d3dfm_DataArray[i].mesh2d_bodsed[-1, :, 2].compute()
|
||||
initialBed[i]= d3dfm_DataArray[i].mesh2d_s1[0, :] - d3dfm_DataArray[i].mesh2d_waterdepth[0, :]
|
||||
finalBed = d3dfm_DataArray[i].mesh2d_s1[-1, :] - d3dfm_DataArray[i].mesh2d_waterdepth[-1, :]
|
||||
|
||||
erodeSed[i] = finalBed - initialBed[i]
|
||||
erodeSed[i] = erodeSed[i].compute()
|
||||
initialBed[i] = initialBed[i].compute()
|
||||
|
||||
# Import concentration of sediment 2 at timestep 95 and layer 10
|
||||
sedConc[i] = d3dfm_DataArray[i].mesh2d_sedfrac_concentration[94, 2, :, 9].compute()
|
||||
|
||||
modelCRS[i] = vars_pd['EPSG_code']['projected_coordinate_system']
|
||||
|
||||
#%% Define axis limits
|
||||
axLim_Xmin = []
|
||||
axLim_Xmax = []
|
||||
|
||||
axLim_Ymin = []
|
||||
axLim_Ymax = []
|
||||
|
||||
# Set axis limits
|
||||
# Full domain
|
||||
axLim_Xmin.append(298500)
|
||||
axLim_Xmax.append(306800)
|
||||
|
||||
axLim_Ymin.append(58500)
|
||||
axLim_Ymax.append(68000)
|
||||
|
||||
# Zoom North River
|
||||
axLim_Xmin.append(303500)
|
||||
axLim_Xmax.append(305500)
|
||||
|
||||
axLim_Ymin.append(66000)
|
||||
axLim_Ymax.append(68000)
|
||||
|
||||
# Zoom Tribs
|
||||
axLim_Xmin.append(305500)
|
||||
axLim_Xmax.append(306800)
|
||||
|
||||
axLim_Ymin.append(60000)
|
||||
axLim_Ymax.append(62500)
|
||||
|
||||
# Zoom far trib
|
||||
axLim_Xmin.append(305800)
|
||||
axLim_Xmax.append(305900)
|
||||
|
||||
axLim_Ymin.append(60150)
|
||||
axLim_Ymax.append(60400)
|
||||
|
||||
|
||||
#%% Plot using DFM functions
|
||||
|
||||
for i in modelPlot:
|
||||
# Create figure
|
||||
fig, axesFig = plt.subplots(1, 4, figsize=(8, 2))
|
||||
|
||||
axes = axesFig.flatten()
|
||||
|
||||
# Loop through axis and plot with different limits
|
||||
for subIDX in range(0, 4):
|
||||
|
||||
# # Plot Sediment Mass
|
||||
# smp = sedMass[i].ugrid.plot(ax=axes[subIDX], edgecolors='face', cmap='turbo',
|
||||
# vmin=0, vmax=2000, add_colorbar=False)
|
||||
|
||||
# # Plot Erosion
|
||||
# smp = erodeSed[i].ugrid.plot(ax=axes[subIDX], edgecolors='face', cmap='coolwarm',
|
||||
# vmin=-1, vmax=1, add_colorbar=False)
|
||||
|
||||
# Plot Bed
|
||||
smp = initialBed[i].ugrid.plot(ax=axes[subIDX], edgecolors='face', cmap='nipy_spectral',
|
||||
vmin=-25, vmax=0, add_colorbar=False)
|
||||
|
||||
|
||||
# Set axis labels
|
||||
axes[subIDX].set_xlabel('')
|
||||
axes[subIDX].set_ylabel('')
|
||||
axes[subIDX].set_title('')
|
||||
axes[subIDX].set_xticks([])
|
||||
axes[subIDX].set_yticks([])
|
||||
|
||||
axes[subIDX].set_xlim(axLim_Xmin[subIDX], axLim_Xmax[subIDX])
|
||||
axes[subIDX].set_ylim(axLim_Ymin[subIDX], axLim_Ymax[subIDX])
|
||||
|
||||
# Add basemap
|
||||
# ctx.add_basemap(ax=axes, source=ctx.providers.Esri.WorldImagery, crs=modelCRS[i], attribution=False)
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
ctx.add_basemap(axes[subIDX], source=mapbox, crs=modelCRS[i])
|
||||
|
||||
|
||||
# cbar = plt.colorbar(smp, ax=axesFig.ravel().tolist(), label='Sand Mass [kg/m2]')
|
||||
# cbar = plt.colorbar(smp, ax=axesFig.ravel().tolist(), label='Erosion/ deposition [m]')
|
||||
cbar = plt.colorbar(smp, ax=axesFig.ravel().tolist(), label='Initial Bed [mNAVD88]')
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/SedMass_' + runLog['Run'][i] + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/ErodeDep_' + runLog['Run'][i] + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/InitialBed_' + runLog['Run'][i] + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Plot using DFM functions
|
||||
#Cmap Path
|
||||
cmap_path = 'C:/Users/arey/Repo/MATLAB_Q/Downloads/KeyColormaps/'
|
||||
|
||||
for i in modelPlot:
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, subplot_kw={'projection': ccrs.epsg(32118)}, figsize=(6, 6))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
fig.tight_layout(pad=3.0)
|
||||
|
||||
# Load cmaps
|
||||
cmap = 'AJMR_Sed5_RevE.xml'
|
||||
plotcmap = cm.make_cmap(cmap_path + cmap)
|
||||
|
||||
pc = plot_netmapdata(ugrid_all[i].verts, values=modelMaxShear[i][0, :],
|
||||
ax=axes, linewidth=0.5, cmap=plotcmap)
|
||||
|
||||
axes.set_xlim(305900, 306400)
|
||||
axes.set_ylim(61500, 62200)
|
||||
|
||||
# axes.quiver(modelX[i], modelX[i], U[i], V[i], color='w', scale=1.00)
|
||||
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32118')
|
||||
|
||||
extent = axes.get_extent()
|
||||
axes.set_xticks(np.linspace(extent[0], extent[1], 4))
|
||||
axes.set_yticks(np.linspace(extent[2], extent[3], 4))
|
||||
|
||||
pc.set_clim([0, 0.145])
|
||||
cbarTicks = [0, 0.0378, 0.063, 0.0826, 0.11, 0.145]
|
||||
cbar = fig.colorbar(pc, ax=axes, shrink=0.95,
|
||||
ticks=cbarTicks)
|
||||
cbar.set_label('Total Max Shear Stress [N/m^2]')
|
||||
|
||||
# Add label on colorbar
|
||||
cbar.ax.text(0.08, (cbarTicks[0]+cbarTicks[1])/2, 'CLAY', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
cbar.ax.text(0.08, (cbarTicks[1]+cbarTicks[2])/2, 'FSILT', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
cbar.ax.text(0.08, (cbarTicks[2]+cbarTicks[3])/2, 'MSILT', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
cbar.ax.text(0.08, (cbarTicks[3]+cbarTicks[4])/2, 'CSILT', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
cbar.ax.text(0.08, (cbarTicks[4]+cbarTicks[5])/2, 'FSAND', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs/Shear_Class_' + runLog['Run'][i] + '.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Import Model results for animations
|
||||
|
||||
# modelPlot = [20, 22]
|
||||
# modelPlot = [18, 20, 21, 22]
|
||||
# modelPlot = [18, 21]
|
||||
modelPlot = [25, 26]
|
||||
modelPlot = [146]
|
||||
|
||||
modelSedConc = [None] * (max(modelPlot)+1)
|
||||
modelMaxShear_animate = [None] * (max(modelPlot)+1)
|
||||
ugrid_all = [None] * (max(modelPlot)+1)
|
||||
d3dfm_DataArray = [None] * (max(modelPlot)+1)
|
||||
modelVelX = [None] * (max(modelPlot)+1)
|
||||
modelVelY = [None] * (max(modelPlot)+1)
|
||||
tSteps = [None] * (max(modelPlot)+1)
|
||||
|
||||
# Time steps to import
|
||||
tStepsImport = range(1000, 7000)
|
||||
|
||||
for i in modelPlot:
|
||||
# Create variables
|
||||
modelVelX[i] = [None] * (max(tStepsImport)+1)
|
||||
modelVelY[i] = [None] * (max(tStepsImport)+1)
|
||||
|
||||
file_nc_map = Path(runLog['Run Location'][i]) / dataPath
|
||||
|
||||
d3dfm_DataArray[i] = dfmt.open_partitioned_dataset(file_nc_map.as_posix())
|
||||
|
||||
# Get Var info
|
||||
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_map.as_posix())
|
||||
vars_pd = dfmt.get_ncvarproperties(d3dfm_DataArray[i])
|
||||
|
||||
# Import
|
||||
tSteps[i] = get_timesfromnc(file_nc=file_nc_map.as_posix(), varname='time')
|
||||
# Get time steps
|
||||
tSteps[i] = d3dfm_DataArray[i].time.values
|
||||
|
||||
ugrid_all[i] = get_netdata(file_nc=file_nc_map.as_posix())
|
||||
modelSedConc[i] = get_ncmodeldata(file_nc=file_nc_map.as_posix(), varname='mesh2d_sedfrac_concentration',
|
||||
timestep='all', station=0, layer=0) #timestep='all' OR timestep=range(0, 30)
|
||||
modelMaxShear_animate[i] = get_ncmodeldata(file_nc=file_nc_map.as_posix(), varname='mesh2d_tausmax',
|
||||
timestep='all')
|
||||
# Loop through time steps and import data
|
||||
for t in tStepsImport:
|
||||
# Import 3D velocity data
|
||||
modelVelXin = d3dfm_DataArray[i].mesh2d_ucx[t, :, :].compute()
|
||||
modelVelYin = d3dfm_DataArray[i].mesh2d_ucy[t, :, :].compute()
|
||||
|
||||
# Average over layers
|
||||
modelVelX[i][t] = modelVelXin.mean(axis=1)
|
||||
modelVelY[i][t] = modelVelYin.mean(axis=1)
|
||||
|
||||
# %% Import water levels from hist
|
||||
print(t)
|
||||
|
||||
# %% Import water levels from hist
|
||||
modelHistWL = [None] * (max(modelPlot)+1)
|
||||
modelHistTime = [None] * (max(modelPlot)+1)
|
||||
# tSteps = [None] * (max(modelPlot)+1)
|
||||
|
||||
for i in modelPlot:
|
||||
# Hist file path
|
||||
file_nc_hist = Path(runLog['Run Location'][i]) / histPath
|
||||
|
||||
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_hist.as_posix())
|
||||
# Open hist file dataset
|
||||
data_xr = xr.open_mfdataset(file_nc_hist, preprocess=dfmt.preprocess_hisnc)
|
||||
|
||||
# Get station names
|
||||
histStations = get_hisstationlist(file_nc=file_nc_hist.as_posix(), varname='waterlevel')
|
||||
# Get stations
|
||||
stations_pd = data_xr['stations'].to_dataframe()
|
||||
|
||||
# Import
|
||||
modelHistTime[i] = get_timesfromnc(file_nc=file_nc_hist.as_posix(), varname='time')
|
||||
modelHistWL[i] = get_ncmodeldata(file_nc=file_nc_hist.as_posix(), varname='waterlevel',
|
||||
timestep='all', station=1)
|
||||
# Convert to Pandas
|
||||
modelHistWL[i] = data_xr.waterlevel.sel(stations=['West_WL', 'East_WL']).to_pandas()
|
||||
|
||||
# Put in UTC if needed
|
||||
if i == 104 or i == 105 or i >= 135:
|
||||
modelHistWL[i] = modelHistWL[i].tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
else:
|
||||
modelHistWL[i] = modelHistWL[i].tz_localize(
|
||||
pytz.timezone('UTC'))
|
||||
|
||||
|
||||
#%% Sediment animations
|
||||
#%% Velocity Quiver animations
|
||||
plt.rcParams['animation.ffmpeg_path'] = \
|
||||
'C:/Users/arey/Local/ffmpeg-2022-02-14-git-59c647bcf3-full_build/bin/ffmpeg.exe'
|
||||
'C:/Users/arey/files/Local/ffmpeg-2022-02-14-git-59c647bcf3-full_build/bin/ffmpeg.exe'
|
||||
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckex9vtri0o6619p55sl5qiyv/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
x, y, arrow_length = 0.93, 0.95, 0.12
|
||||
# fontprops = fm.FontProperties(size=12)
|
||||
|
||||
# Set model to plot
|
||||
# modelPlot = [20, 22]
|
||||
# modelPlot = [18, 20, 21, 22]
|
||||
modelPlot = [25, 26]
|
||||
cmap_path = 'C:/Users/arey/Repo/MATLAB_Q/Downloads/KeyColormaps/'
|
||||
modelPlot = [84]
|
||||
tStepsPlot = tStepsImport
|
||||
tStepsPlot = range(970, 971)
|
||||
tStepsPlot = range(955, 990)
|
||||
|
||||
|
||||
vmin = 0 # Legend min
|
||||
vmax = 0.05 # Legend max
|
||||
scale = 30 # Size of arrows, smaller is bigger #25
|
||||
qmax = 0.002# Scaling threshold. Values above this are set to 1
|
||||
amin = 0.1 # Minimum arrow length. A dot is used below this value
|
||||
|
||||
for m in modelPlot:
|
||||
vmax = 0.75
|
||||
vmin = 0
|
||||
# vmax = 0.145
|
||||
# vmin = 0
|
||||
|
||||
cbarTicks = [0, 0.0378, 0.063, 0.0826, 0.11, 0.145]
|
||||
# Find water level plot range
|
||||
wlPlotMinIDX = wlHistTimeIDX = np.argmin(abs(tSteps[m][tStepsPlot[0]] - modelHistWL[m].index.values))
|
||||
wlPlotMaxIDX = wlHistTimeIDX = np.argmin(abs(tSteps[m][tStepsPlot[-1]] - modelHistWL[m].index.values))
|
||||
|
||||
cmapName = 'turbo'
|
||||
cmap = mpl.cm.turbo
|
||||
# cmapName = 'AJMR_Sed5_RevE.xml'
|
||||
# cmap = cm.make_cmap(cmap_path + cmapName)
|
||||
|
||||
|
||||
# Zoom limits
|
||||
# # Zoom limits
|
||||
# xLimits = [305900, 306500]
|
||||
# yLimits = [61400, 62200]
|
||||
|
||||
# Wide Limits
|
||||
xLimits = [302719.4, 306934.2]
|
||||
yLimits = [60263.7, 64194.8]
|
||||
# Corner limits
|
||||
xLimits = [306088, 306444]
|
||||
yLimits = [61370, 61741]
|
||||
|
||||
# Setup Video
|
||||
metadata = dict(title='NTC Sediment Animation', artist='Matplotlib',
|
||||
comment='AJMR June 28, 2022')
|
||||
writer = animation.FFMpegWriter(fps=2, metadata=metadata, codec='h264', bitrate=5000)
|
||||
fig, axes = plt.subplots(figsize=(6, 6))
|
||||
metadata = dict(title='NTC Velocity', artist='Matplotlib',
|
||||
comment='AJMR June 2, 2023')
|
||||
writer = animation.FFMpegWriter(fps=1, metadata=metadata, codec='h264', bitrate=5000)
|
||||
fig, axes = plt.subplots(figsize=(10, 10))
|
||||
|
||||
writer.setup(fig, '//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/' +
|
||||
'06_Models/04_Delft3D/Figures/SedConc/HQ3_Wide_RevC_Long_Sed_Cond_'
|
||||
+ runLog['Run'][m] + '.mp4')
|
||||
writer.setup(fig, 'C:/Users/arey/files/Projects/Newtown/Vel_Animation4.mp4')
|
||||
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
for i in np.arange(1, len(modelSedConc[m])-1, 1): #289
|
||||
frameCount = 1
|
||||
|
||||
for t in tStepsPlot: #289
|
||||
# Clear axes for video
|
||||
axes.cla()
|
||||
|
||||
# Create new figure for stills
|
||||
# fig, axes = plt.subplots(figsize=(6, 6))
|
||||
# fig, axes = plt.subplots(figsize=(12, 12))
|
||||
|
||||
# Clear inset WL Plot
|
||||
# if i > 1:
|
||||
# axin1.cla()
|
||||
if frameCount > 1:
|
||||
axin1.cla()
|
||||
|
||||
# Plot sediment
|
||||
pc = plot_netmapdata(ugrid_all[m].verts, values=modelSedConc[m][i, 0, :, 0],
|
||||
ax=axes, cmap=cmapName,
|
||||
antialiaseds=False)
|
||||
|
||||
# Plot shear
|
||||
# pc = plot_netmapdata(ugrid_all[m].verts, values=modelMaxShear_animate[m][i,:],
|
||||
# ax=axes, cmap=cmap,
|
||||
# antialiaseds=False)
|
||||
# Cropt to zoom limits
|
||||
croppedGridMask = (d3dfm_DataArray[m].mesh2d_face_x >= xLimits[0]) & \
|
||||
(d3dfm_DataArray[m].mesh2d_face_x <= xLimits[1]) & \
|
||||
(d3dfm_DataArray[m].mesh2d_face_y >= yLimits[0]) & \
|
||||
(d3dfm_DataArray[m].mesh2d_face_y <= yLimits[1])
|
||||
croppedGridMask = croppedGridMask.compute()
|
||||
|
||||
# Rasterize Ugrid
|
||||
rasterU = dfmt.rasterize_ugrid(d3dfm_DataArray[m]['mesh2d_ucx'].isel(
|
||||
time=t, mesh2d_nLayers=4).where(croppedGridMask, drop=True).to_dataset(), resolution=10)
|
||||
rasterV = dfmt.rasterize_ugrid(d3dfm_DataArray[m]['mesh2d_ucy'].isel(
|
||||
time=t, mesh2d_nLayers=4).where(croppedGridMask, drop=True).to_dataset(), resolution=10)
|
||||
|
||||
# Using fixed grid-> select desiered time step/layer/variable, convert to dataset, rasterize, then to numpy
|
||||
overGrid_U_Scale = rasterU.to_array().to_numpy().squeeze()
|
||||
overGrid_V_Scale = rasterV.to_array().to_numpy().squeeze()
|
||||
|
||||
r = np.power(np.add(np.power(overGrid_U_Scale, 2), np.power(overGrid_V_Scale, 2)), 0.5)
|
||||
|
||||
curPlotMask = np.sqrt((overGrid_U_Scale) ** 2 + (overGrid_V_Scale) ** 2) > qmax
|
||||
|
||||
overGrid_U_Plot = np.zeros(overGrid_U_Scale.shape)
|
||||
overGrid_V_Plot = np.zeros(overGrid_V_Scale.shape)
|
||||
|
||||
# Normalize arrows to 1 where not zero
|
||||
overGrid_U_Plot[r != 0] = overGrid_U_Scale[r != 0] / r[r != 0]
|
||||
overGrid_V_Plot[r != 0] = overGrid_V_Scale[r != 0] / r[r != 0]
|
||||
|
||||
# Scale arrows above qmax
|
||||
overGrid_U_Plot[~curPlotMask] = overGrid_U_Plot[~curPlotMask] * (r[~curPlotMask] / qmax)
|
||||
overGrid_V_Plot[~curPlotMask] = overGrid_V_Plot[~curPlotMask] * (r[~curPlotMask] / qmax)
|
||||
|
||||
# Calculate velocity magnitude
|
||||
velMag = np.sqrt((overGrid_U_Scale) ** 2 + (overGrid_V_Scale) ** 2)
|
||||
|
||||
# Using fixed grid
|
||||
qv = axes.quiver(rasterU['x'].values, rasterU['y'].values, overGrid_U_Plot, overGrid_V_Plot, velMag, scale=scale,
|
||||
color='k', headlength=5, headwidth=5, headaxislength=5, width=0.002, minlength=amin) #200
|
||||
|
||||
# Set map limits
|
||||
axes.set_xlim(xLimits[0], xLimits[1])
|
||||
|
|
@ -353,33 +224,41 @@ for m in modelPlot:
|
|||
# Add basemap
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32118')
|
||||
|
||||
# Set map ticks
|
||||
axes.set_xticks(np.linspace(xLimits[0], xLimits[1], 4))
|
||||
axes.set_yticks(np.linspace(yLimits[0], yLimits[1], 4))
|
||||
|
||||
# Color Limits
|
||||
pc.set_clim([vmin, vmax])
|
||||
qv.set_clim([vmin, vmax])
|
||||
|
||||
# Set colormap for quiver
|
||||
qv.set_cmap(cmap)
|
||||
|
||||
# Add title
|
||||
axes.title.set_text('Bottom Sediment Concentration at: ' + tSteps[m][i].strftime("%Y/%m/%d %H:%M"))
|
||||
# axes.title.set_text('Total Max Shear Stress at: ' + str(tSteps[m][i]))
|
||||
axes.title.set_text('Mid-depth velocity: ' + np.datetime_as_string(tSteps[m][t], unit='m'))
|
||||
|
||||
# Add inset wl plot
|
||||
axin1 = axes.inset_axes([0.1, 0.1, 0.4, 0.15])
|
||||
axin1.plot(modelHistTime[m], modelHistWL[m])
|
||||
# Add current point
|
||||
# Find closest time
|
||||
abs_deltas_from_target_date = np.absolute(modelHistTime[m] - tSteps[m][i])
|
||||
axin1.scatter(modelHistTime[m][np.argmin(abs_deltas_from_target_date)], modelHistWL[m][np.argmin(abs_deltas_from_target_date)], 10, 'r')
|
||||
|
||||
# axin1.set_xticks([modelHistTime[m][0], modelHistTime[m][len(modelHistTime[m])-1]])
|
||||
# Find matching time step in model water level history
|
||||
wlHistTimeIDX = np.argmin(abs(tSteps[m][t] - modelHistWL[m].index.values))
|
||||
|
||||
# Plot model water level history
|
||||
axin1.plot(modelHistWL[m].index, modelHistWL[m])
|
||||
# Add current point
|
||||
axin1.scatter(modelHistWL[m].index[wlHistTimeIDX],
|
||||
modelHistWL[m]['East_WL'][wlHistTimeIDX], 10, 'r')
|
||||
|
||||
axin1.set_xticks([])
|
||||
axin1.set_yticks([])
|
||||
|
||||
# Set time axis limits
|
||||
axin1.set_xlim([modelHistWL[m].index[wlPlotMinIDX],
|
||||
modelHistWL[m].index[wlPlotMaxIDX]])
|
||||
# Set inset axis y limits
|
||||
axin1.set_ylim([-0.5, 2])
|
||||
|
||||
|
||||
plt.setp(axin1.get_xticklabels(), backgroundcolor="white")
|
||||
plt.setp(axin1.get_yticklabels(), backgroundcolor="white")
|
||||
|
||||
if i == 1: #i < 1000
|
||||
if frameCount == 1: #i < 1000
|
||||
fig.subplots_adjust(right=0.8)
|
||||
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
|
||||
|
||||
|
|
@ -387,55 +266,31 @@ for m in modelPlot:
|
|||
# cax = plt.axes([0.82, 0.25, 0.04, 0.5])
|
||||
cax = plt.axes([0.85, 0.15, 0.04, 0.7])
|
||||
|
||||
# Add colorbar with designated tick marks
|
||||
# cb1 = mpl.colorbar.ColorbarBase(cax, cmap=cmap,
|
||||
# norm=norm,
|
||||
# orientation='vertical',
|
||||
# ticks=cbarTicks)
|
||||
|
||||
# Add colorbar without designated tick marks
|
||||
cb1 = mpl.colorbar.ColorbarBase(cax, cmap=cmap,
|
||||
norm=norm,
|
||||
orientation='vertical')
|
||||
|
||||
# Colorbar lavel
|
||||
cb1.set_label('Sediment Concentration [$kg/m^3$]')
|
||||
# cb1.set_label('Total Max Shear Stress [N/m^2]')
|
||||
|
||||
# Add label on colorbar
|
||||
# cb1.ax.text(0.08, (cbarTicks[0] + cbarTicks[1]) / 2, 'CLAY', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
# cb1.ax.text(0.08, (cbarTicks[1] + cbarTicks[2]) / 2, 'FSILT', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
# cb1.ax.text(0.08, (cbarTicks[2] + cbarTicks[3]) / 2, 'MSILT', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
# cb1.ax.text(0.08, (cbarTicks[3] + cbarTicks[4]) / 2, 'CSILT', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
# cb1.ax.text(0.08, (cbarTicks[4] + cbarTicks[5]) / 2, 'FSAND', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
|
||||
|
||||
# Change label font size for only the primary axis
|
||||
for item in ([axes.xaxis.label, axes.yaxis.label] +
|
||||
axes.get_xticklabels() + axes.get_yticklabels()):
|
||||
item.set_fontsize(4)
|
||||
|
||||
cb1.set_label('Water velocity [m/s]')
|
||||
|
||||
# Save video frame
|
||||
writer.grab_frame()
|
||||
# plt.show()
|
||||
print(t)
|
||||
plt.show()
|
||||
print(i)
|
||||
|
||||
# Increase frame count
|
||||
frameCount += 1
|
||||
|
||||
# Save stills
|
||||
fig.savefig('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/' +
|
||||
'06_Models/04_Delft3D/Figures/SedConc/Wide_RevC_SedConc_' +
|
||||
runLog['Run'][m] + '_Frame_' + str(i) + '.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
# fig.savefig('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/' +
|
||||
# '06_Models/04_Delft3D/Figures/ShearStress/Wide_ShearStress_' +
|
||||
# '06_Models/04_Delft3D/Figures/SedConc/Wide_RevC_SedConc_' +
|
||||
# runLog['Run'][m] + '_Frame_' + str(i) + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
|
||||
|
||||
# Save video
|
||||
writer.finish()
|
||||
|
||||
|
|
|
|||
|
|
@ -51,34 +51,19 @@ import datetime as datetime
|
|||
|
||||
#%% Load in Measured data
|
||||
|
||||
dfIN=pd.read_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/21_YSI Data/D1-D9_Combined/Combined_with_2015/All_Combine.csv')
|
||||
YSI_df = pd.read_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/21_YSI Data/D1-D9_Combined/Combined_with_2015/All_Combine.csv')
|
||||
|
||||
# Filter out invalid data
|
||||
df1 = dfIN
|
||||
df1.loc[df1['Temp'] < -30, 'Temp'] = np.nan
|
||||
df1.loc[df1['Sal'] < 0, 'Sal'] = np.nan
|
||||
df1['DateTime'] = pd.to_datetime(df1['Date Time Local'])
|
||||
df1.set_index('DateTime', inplace=True)
|
||||
YSI_df.loc[YSI_df['Temp'] < -30, 'Temp'] = np.nan
|
||||
YSI_df.loc[YSI_df['Sal'] < 0, 'Sal'] = np.nan
|
||||
YSI_df['DateTime'] = pd.to_datetime(YSI_df['Date Time Local'])
|
||||
YSI_df.set_index('DateTime', inplace=True)
|
||||
|
||||
df1.index = df1.index.tz_localize(pytz.timezone('America/New_York'),
|
||||
# Set index as DateTime
|
||||
YSI_df.index = YSI_df.index.tz_localize(pytz.timezone('America/New_York'),
|
||||
ambiguous='NaT', nonexistent='NaT')
|
||||
|
||||
df1.index = df1.index.tz_convert(pytz.utc)
|
||||
|
||||
# Convert to Dataframe
|
||||
YSI_df = pd.DataFrame(df1, columns=['Station', 'Temp', 'Sal', 'Depth'])
|
||||
# Set index as DateTime
|
||||
|
||||
#Change Station of interest (EB043, EK108, NC310, NC313, NC316, NC318)
|
||||
Station=['EB043','EK108','NC310','NC313','NC316','NC318']
|
||||
x=4
|
||||
i=Station[x]
|
||||
|
||||
|
||||
Station_interest=i
|
||||
Top=str(Station_interest)+str('A')
|
||||
Bot=str(Station_interest)+str('C')
|
||||
|
||||
YSI_df.index = YSI_df.index.tz_convert(pytz.utc)
|
||||
|
||||
#%% Load in EFDC Output
|
||||
FILE=r"//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/06_Models/02_EFDC/06_EFDC_Outputs/2014-2015/EFDC_Compiled_2014_2015.xlsx"
|
||||
|
|
@ -135,16 +120,16 @@ NGG2_pd = NGG2_pd.tz_localize(
|
|||
# Drop ambiguous times
|
||||
NGG2_pd = NGG2_pd.dropna()
|
||||
|
||||
|
||||
#%% Load in Delft Results
|
||||
#%% Read in model log
|
||||
runLog = pd.read_excel('C:/Users/arey/files/Projects/Newtown/Model Log NTC.xlsx', 'ModelLog')
|
||||
|
||||
|
||||
#%% Load in Delft Results
|
||||
dataPath = "FlowFM_map.nc"
|
||||
histPath = "FlowFM_his.nc"
|
||||
|
||||
#read in time series
|
||||
modelPlot = [105]#, 106]
|
||||
modelPlot = [105, 139, 141, 142, 143, 144]#, 106]
|
||||
|
||||
Delft_WL = [None] * (max(modelPlot)+1)
|
||||
Delft_T_B = [None] * (max(modelPlot)+1)
|
||||
|
|
@ -157,11 +142,6 @@ Delft_S_T = [None] * (max(modelPlot)+1)
|
|||
for i in modelPlot:
|
||||
file_nc_hist = pl.Path(runLog['Run Location'][i],
|
||||
'FlowFM', 'dflowfm', 'output', 'FlowFM_his.nc')
|
||||
# file_nc_hist = "C:/Users/mrobinson/OneDrive - W.F. Baird & Associates Coastal Engineers Ltd/Documents/11934.202 NTC/06 Models/06 Delft3DFM/Results/Run129/FlowFM_his.nc"
|
||||
|
||||
|
||||
# Hist file path
|
||||
#file_nc_hist = file_nc_hist.as_posix()
|
||||
|
||||
# Open hist file dataset
|
||||
data_xr = xr.open_mfdataset(file_nc_hist, preprocess=dfmt.preprocess_hisnc)
|
||||
|
|
@ -169,7 +149,7 @@ for i in modelPlot:
|
|||
# Get stations
|
||||
stations_pd = data_xr['stations'].to_dataframe()
|
||||
|
||||
# Get observations
|
||||
# Get observation stations
|
||||
#observations_pd=data_xr['stations'].to_dataframe()
|
||||
|
||||
# Convert to Pandas
|
||||
|
|
@ -184,7 +164,7 @@ for i in modelPlot:
|
|||
Delft_S_T[i] = data_xr.salinity.sel(stations=['NC310', 'NC313','NC316','NC318','EB043','EK108'], laydim=9).to_pandas()
|
||||
|
||||
# Put in UTC if needed
|
||||
if i == 104 or i == 105:
|
||||
if i == 104 or i == 105 or i >= 135:
|
||||
Delft_WL[i] = Delft_WL[i].tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
|
||||
|
|
@ -199,7 +179,7 @@ for i in modelPlot:
|
|||
|
||||
|
||||
#%% Plot Water Levels
|
||||
i = 105
|
||||
i = 142
|
||||
|
||||
# Create Figure
|
||||
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(18, 8), sharex=False, sharey=True)
|
||||
|
|
@ -212,19 +192,21 @@ NGG2_pd['water_surface_elevation'].multiply(0.3048).plot(label='FFG', color='k',
|
|||
Delft_WL[i]['West_WL'].plot(label='Delft', color='r', linewidth=2)
|
||||
|
||||
# Plot YSI
|
||||
YSI_df.loc[YSI_df['Station'] == 'NC318A', 'Depth'].add(-1.3).plot(
|
||||
label='YSI', color='b', linewidth=2, ax=ax)
|
||||
# YSI_df.loc[YSI_df['Station'] == 'NC318A', 'Depth'].add(-1.3).plot(
|
||||
# label='YSI', color='b', linewidth=2, ax=ax)
|
||||
|
||||
# Plot EFDC
|
||||
EFDC_dat['NC318A'].loc[:, 'WSE_m'].plot(label='EFDC', color='m', linewidth=2, ax=ax)
|
||||
# EFDC_dat['NC318A'].loc[:, 'WSE_m'].plot(label='EFDC', color='m', linewidth=2, ax=ax)
|
||||
|
||||
# Plot Telemac
|
||||
TM_dat['NC318'].loc[:, 'WSE_m'].plot(label='EFDC', color='m', linewidth=2, ax=ax)
|
||||
# TM_dat['NC318'].loc[:, 'WSE_m'].plot(label='EFDC', color='m', linewidth=2, ax=ax)
|
||||
|
||||
|
||||
# Set Time Bounds
|
||||
dateMin = pd.to_datetime(datetime.datetime(2014, 11, 1, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2014, 11, 13, 0, 0, 0, 0), utc=True)
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2014, 11, 1, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2014, 11, 13, 0, 0, 0, 0), utc=True)
|
||||
dateMin = pd.to_datetime(datetime.datetime(2015, 7, 15, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2015, 9, 1, 0, 0, 0, 0), utc=True)
|
||||
|
||||
# Set X Axis
|
||||
ax.set_xlim(dateMin, dateMax)
|
||||
|
|
@ -255,12 +237,16 @@ fig, ax = plt.subplots(nrows=2, ncols=6, figsize=(16, 7), sharex=True, sharey=Tr
|
|||
# fig.suptitle('Comparison of Predicted and Measured Temperature at NC316', fontsize=18)
|
||||
|
||||
# Set Time Bounds for all axis since linked
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2015, 7, 15, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2015, 9, 1, 0, 0, 0, 0), utc=True)
|
||||
dateMin = pd.to_datetime(datetime.datetime(2015, 7, 15, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2015, 9, 1, 0, 0, 0, 0), utc=True)
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2015, 7, 28, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2015, 8, 7, 0, 0, 0, 0), utc=True)
|
||||
dateMin = pd.to_datetime(datetime.datetime(2014, 7, 1, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2015, 10, 1, 0, 0, 0, 0), utc=True)
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2014, 7, 1, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2015, 10, 1, 0, 0, 0, 0), utc=True)
|
||||
|
||||
# Set up Delft3D Variables
|
||||
DelftInterp = [None] * (max(modelPlot)+1)
|
||||
RMSE_D = [None] * (max(modelPlot)+1)
|
||||
|
||||
|
||||
# Loop through stations and plot
|
||||
|
|
@ -271,17 +257,22 @@ for StatIDX, Stat in enumerate(Stations):
|
|||
ObsDat = YSI_df.loc[YSI_df['Station'] == Stat + 'A', 'Temp']
|
||||
ObsDat = ObsDat.loc[(ObsDat.index > dateMin) & (ObsDat.index <= dateMax)]
|
||||
|
||||
DelftInterp = resampleToObs(Delft_T_T[i][Stat], ObsDat, '5T')
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'TEM10'].resample('10T').nearest(), ObsDat, '5T')
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'TEM10'].resample('15T').nearest(), ObsDat, '5T')
|
||||
# Loop through Delft Runs
|
||||
for i in modelPlot:
|
||||
DelftInterp[i] = resampleToObs(Delft_T_T[i][Stat], ObsDat, '5T')
|
||||
RMSE_D[i] = syncModelRMSE(DelftInterp[i], ObsDat)
|
||||
|
||||
RMSE_D = syncModelRMSE(DelftInterp, ObsDat)
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'TEM10'].resample('10T').nearest(), ObsDat, '5T')
|
||||
RMSE_E = syncModelRMSE(EFDCInterp, ObsDat)
|
||||
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'TEM10'].resample('15T').nearest(), ObsDat, '5T')
|
||||
# RMSE_T = syncModelRMSE(TMInterp, ObsDat)
|
||||
|
||||
# Plot Top Layer Temperature
|
||||
ObsDat.plot(label='YSI', color='k', linewidth=2, ax=ax[0, StatIDX])
|
||||
ax[0, StatIDX].plot(Delft_WL[i].index, Delft_T_T[i][Stat], linestyle='-', label='Delft3D: ' + str(round(RMSE_D, 2)) + degree_sign + 'C')
|
||||
for i in modelPlot:
|
||||
ax[0, StatIDX].plot(Delft_WL[i].index, Delft_T_T[i][Stat], linestyle='-', label='Delft3D_' + str(i) + ':' + str(round(RMSE_D[i], 2)) + degree_sign + 'C')
|
||||
|
||||
EFDC_dat[Stat].loc[:, 'TEM10'].plot(linewidth=1, ax=ax[0, StatIDX], label='EFDC: ' + str(round(RMSE_E, 2)) + degree_sign + 'C')
|
||||
# TM_dat[Stat].loc[:, 'TEM10'].plot(linewidth=1, ax=ax[0, StatIDX], label='Telemac: ' + str(round(RMSE_T, 2)) + degree_sign + 'C')
|
||||
|
||||
|
|
@ -293,16 +284,20 @@ for StatIDX, Stat in enumerate(Stations):
|
|||
ObsDat = YSI_df.loc[YSI_df['Station'] == Stat + 'C', 'Temp']
|
||||
ObsDat = ObsDat.loc[(ObsDat.index > dateMin) & (ObsDat.index <= dateMax)]
|
||||
|
||||
DelftInterp = resampleToObs(Delft_T_B[i][Stat], ObsDat, '5T')
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'TEM1'].resample('10T').nearest(), ObsDat, '5T')
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'TEM1'].resample('15T').nearest(), ObsDat, '5T')
|
||||
# Loop through Delft Runs
|
||||
for i in modelPlot:
|
||||
DelftInterp[i] = resampleToObs(Delft_T_B[i][Stat], ObsDat, '5T')
|
||||
RMSE_D[i] = syncModelRMSE(DelftInterp[i], ObsDat)
|
||||
|
||||
RMSE_D = syncModelRMSE(DelftInterp, ObsDat)
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'TEM1'].resample('10T').nearest(), ObsDat, '5T')
|
||||
RMSE_E = syncModelRMSE(EFDCInterp, ObsDat)
|
||||
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'TEM1'].resample('15T').nearest(), ObsDat, '5T')
|
||||
# RMSE_T = syncModelRMSE(TMInterp, ObsDat)
|
||||
|
||||
ObsDat.plot(label='YSI', color='k', linewidth=2, ax=ax[1, StatIDX])
|
||||
ax[1, StatIDX].plot(Delft_WL[i].index, Delft_T_B[i][Stat], linestyle='-', label='Delft3D: ' + str(round(RMSE_D, 2)) + degree_sign + 'C')
|
||||
for i in modelPlot:
|
||||
ax[1, StatIDX].plot(Delft_WL[i].index, Delft_T_B[i][Stat], linestyle='-', label='Delft3D_' + str(i) + ':' + str(round(RMSE_D[i], 2)) + degree_sign + 'C')
|
||||
EFDC_dat[Stat].loc[:, 'TEM1'].plot(linewidth=1, ax=ax[1, StatIDX], label='EFDC: ' + str(round(RMSE_E, 2)) + degree_sign + 'C')
|
||||
# TM_dat[Stat].loc[:, 'TEM1'].plot(linewidth=1, ax=ax[1, StatIDX], label='Telemac: ' + str(round(RMSE_T, 2)) + degree_sign + 'C')
|
||||
|
||||
|
|
@ -327,23 +322,26 @@ plt.tight_layout()
|
|||
plt.show()
|
||||
|
||||
# Save figure
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/TempSalFigs/Temperature_All.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/TempSalFigs/Temperature_All_SalSens.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
|
||||
#%% Plot Salinity AJMR
|
||||
Stations=['NC310', 'NC313', 'NC316', 'NC318', 'EB043', 'EK108']
|
||||
|
||||
# Create Figure
|
||||
fig, ax = plt.subplots(nrows=2, ncols=6, figsize=(16, 7), sharex=True, sharey=True)
|
||||
fig, ax = plt.subplots(nrows=2, ncols=6, figsize=(16, 10), sharex=True, sharey=True)
|
||||
|
||||
|
||||
# Set Time Bounds for all axis since linked
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2015, 7, 15, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2015, 9, 1, 0, 0, 0, 0), utc=True)
|
||||
dateMin = pd.to_datetime(datetime.datetime(2015, 7, 28, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2015, 8, 7, 0, 0, 0, 0), utc=True)
|
||||
dateMin = pd.to_datetime(datetime.datetime(2015, 7, 15, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2015, 9, 1, 0, 0, 0, 0), utc=True)
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2015, 7, 28, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2015, 8, 7, 0, 0, 0, 0), utc=True)
|
||||
|
||||
# Set up Delft3D Variables
|
||||
DelftInterp = [None] * (max(modelPlot)+1)
|
||||
RMSE_D = [None] * (max(modelPlot)+1)
|
||||
|
||||
# Loop through stations and plot
|
||||
for StatIDX, Stat in enumerate(Stations):
|
||||
|
|
@ -353,17 +351,22 @@ for StatIDX, Stat in enumerate(Stations):
|
|||
ObsDat = YSI_df.loc[YSI_df['Station'] == Stat + 'A', 'Sal']
|
||||
ObsDat = ObsDat.loc[(ObsDat.index > dateMin) & (ObsDat.index <= dateMax)]
|
||||
|
||||
DelftInterp = resampleToObs(Delft_S_T[i][Stat], ObsDat, '5T')
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'SAL10'].resample('10T').nearest(), ObsDat, '5T')
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'SAL10'].resample('15T').nearest(), ObsDat, '5T')
|
||||
# Loop through Delft Runs
|
||||
for i in modelPlot:
|
||||
DelftInterp[i] = resampleToObs(Delft_S_T[i][Stat], ObsDat, '5T')
|
||||
RMSE_D[i] = syncModelRMSE(DelftInterp[i], ObsDat)
|
||||
|
||||
RMSE_D = syncModelRMSE(DelftInterp, ObsDat)
|
||||
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'SAL10'].resample('10T').nearest(), ObsDat, '5T')
|
||||
RMSE_E = syncModelRMSE(EFDCInterp, ObsDat)
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'SAL10'].resample('15T').nearest(), ObsDat, '5T')
|
||||
# RMSE_T = syncModelRMSE(TMInterp, ObsDat)
|
||||
|
||||
# Plot Top Layer Salinity
|
||||
ObsDat.plot(label='YSI', color='k', linewidth=2, ax=ax[0, StatIDX])
|
||||
ax[0, StatIDX].plot(Delft_S_T[i].index, Delft_S_T[i][Stat], linestyle='-', label='Delft3D: ' + str(round(RMSE_D, 2)) + ' psu')
|
||||
for i in modelPlot:
|
||||
ax[0, StatIDX].plot(Delft_S_T[i].index, Delft_S_T[i][Stat], linestyle='-', label='Delft3D_' + str(i) + ':' + str(round(RMSE_D[i], 2)) + ' psu')
|
||||
|
||||
EFDC_dat[Stat].loc[:, 'SAL10'].plot(linewidth=1, ax=ax[0, StatIDX], label='EFDC: ' + str(round(RMSE_E, 2)) + ' psu')
|
||||
# TM_dat[Stat].loc[:, 'SAL10'].plot(linewidth=1, ax=ax[0, StatIDX], label='Telemac: ' + str(round(RMSE_T, 2)) + ' psu')
|
||||
|
||||
|
|
@ -373,17 +376,19 @@ for StatIDX, Stat in enumerate(Stations):
|
|||
ObsDat = YSI_df.loc[YSI_df['Station'] == Stat + 'C', 'Sal']
|
||||
ObsDat = ObsDat.loc[(ObsDat.index > dateMin) & (ObsDat.index <= dateMax)]
|
||||
|
||||
DelftInterp = resampleToObs(Delft_S_B[i][Stat], ObsDat, '5T')
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'SAL1'].resample('10T').nearest(), ObsDat, '5T')
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'SAL1'].resample('15T').nearest(), ObsDat, '5T')
|
||||
for i in modelPlot:
|
||||
DelftInterp[i] = resampleToObs(Delft_S_B[i][Stat], ObsDat, '5T')
|
||||
RMSE_D[i] = syncModelRMSE(DelftInterp[i], ObsDat)
|
||||
|
||||
RMSE_D = syncModelRMSE(DelftInterp, ObsDat)
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'SAL1'].resample('10T').nearest(), ObsDat, '5T')
|
||||
RMSE_E = syncModelRMSE(EFDCInterp, ObsDat)
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'SAL1'].resample('15T').nearest(), ObsDat, '5T')
|
||||
# RMSE_T = syncModelRMSE(TMInterp, ObsDat)
|
||||
|
||||
# Plot Bottom Layer Salinity
|
||||
ObsDat.plot(label='YSI', color='k', linewidth=2, ax=ax[1, StatIDX])
|
||||
ax[1, StatIDX].plot(Delft_S_B[i].index, Delft_S_B[i][Stat], linestyle='-', label='Delft3D: ' + str(round(RMSE_D, 2)) + ' psu')
|
||||
for i in modelPlot:
|
||||
ax[1, StatIDX].plot(Delft_S_B[i].index, Delft_S_B[i][Stat], linestyle='-', label='Delft3D_' + str(i) + ':' + str(round(RMSE_D[i], 2)) + ' psu')
|
||||
EFDC_dat[Stat].loc[:, 'SAL1'].plot(linewidth=1, ax=ax[1, StatIDX], label='EFDC: ' + str(round(RMSE_E, 2)) + ' psu')
|
||||
# TM_dat[Stat].loc[:, 'SAL1'].plot(linewidth=1, ax=ax[1, StatIDX], label='Telemac: ' + str(round(RMSE_T, 2)) + 'psu')
|
||||
|
||||
|
|
@ -397,7 +402,7 @@ ax[0, 0].xaxis.set_major_formatter(
|
|||
mpl_dates.ConciseDateFormatter(ax[0, 0].xaxis.get_major_locator()))
|
||||
|
||||
# Set Y Axis
|
||||
ax[0, 0].set_ylim(12, 27)
|
||||
ax[0, 0].set_ylim(4, 27)
|
||||
ax[0, 0].set_ylabel('Salinity Top (psu)')
|
||||
ax[1, 0].set_ylabel('Salinity Bottom (psu)')
|
||||
|
||||
|
|
@ -406,6 +411,6 @@ plt.tight_layout()
|
|||
plt.show()
|
||||
|
||||
# Save figure
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/TempSalFigs/Salinity_2015_Zoom.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/TempSalFigs/Salinity_2015_Zoom_SensTests.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,13 @@
|
|||
<component name="InspectionProjectProfileManager">
|
||||
<profile version="1.0">
|
||||
<option name="myName" value="Project Default" />
|
||||
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
|
||||
<option name="ignoredIdentifiers">
|
||||
<list>
|
||||
<option value="bool.*" />
|
||||
<option value="geopandas.io.file" />
|
||||
</list>
|
||||
</option>
|
||||
</inspection_tool>
|
||||
</profile>
|
||||
</component>
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
<component name="InspectionProjectProfileManager">
|
||||
<settings>
|
||||
<option name="USE_PROJECT_PROFILE" value="false" />
|
||||
<version value="1.0" />
|
||||
</settings>
|
||||
</component>
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (SatBathy)" project-jdk-type="Python SDK" />
|
||||
</project>
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectModuleManager">
|
||||
<modules>
|
||||
<module fileurl="file://$PROJECT_DIR$/.idea/SatBathy.iml" filepath="$PROJECT_DIR$/.idea/SatBathy.iml" />
|
||||
</modules>
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="VcsDirectoryMappings">
|
||||
<mapping directory="$PROJECT_DIR$/.." vcs="Git" />
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="AutoImportSettings">
|
||||
<option name="autoReloadType" value="SELECTIVE" />
|
||||
</component>
|
||||
<component name="ChangeListManager">
|
||||
<list default="true" id="d03dfa35-c646-4eed-8ec0-e214a894c187" name="Changes" comment="">
|
||||
<change afterPath="$PROJECT_DIR$/../EWR_D3D/WSP_DB.py" afterDir="false" />
|
||||
<change beforePath="$PROJECT_DIR$/../EWR_D3D/EWR_PlotHD_D3D_Profile.py" beforeDir="false" afterPath="$PROJECT_DIR$/../EWR_D3D/EWR_PlotHD_D3D_Profile.py" afterDir="false" />
|
||||
</list>
|
||||
<option name="SHOW_DIALOG" value="false" />
|
||||
<option name="HIGHLIGHT_CONFLICTS" value="true" />
|
||||
<option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
|
||||
<option name="LAST_RESOLUTION" value="IGNORE" />
|
||||
</component>
|
||||
<component name="Git.Settings">
|
||||
<option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$/.." />
|
||||
</component>
|
||||
<component name="ProjectId" id="2PZEhYjwtyRC92jPsj6OLrHGCZ3" />
|
||||
<component name="ProjectLevelVcsManager" settingsEditedManually="true" />
|
||||
<component name="ProjectViewState">
|
||||
<option name="hideEmptyMiddlePackages" value="true" />
|
||||
<option name="showLibraryContents" value="true" />
|
||||
</component>
|
||||
<component name="PropertiesComponent"><![CDATA[{
|
||||
"keyToString": {
|
||||
"RunOnceActivity.OpenProjectViewOnStart": "true",
|
||||
"RunOnceActivity.ShowReadmeOnStart": "true",
|
||||
"WebServerToolWindowFactoryState": "false",
|
||||
"node.js.detected.package.eslint": "true",
|
||||
"node.js.detected.package.tslint": "true",
|
||||
"node.js.selected.package.eslint": "(autodetect)",
|
||||
"node.js.selected.package.tslint": "(autodetect)"
|
||||
}
|
||||
}]]></component>
|
||||
<component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
|
||||
<component name="TaskManager">
|
||||
<task active="true" id="Default" summary="Default task">
|
||||
<changelist id="d03dfa35-c646-4eed-8ec0-e214a894c187" name="Changes" comment="" />
|
||||
<created>1683656322651</created>
|
||||
<option name="number" value="Default" />
|
||||
<option name="presentableId" value="Default" />
|
||||
<updated>1683656322651</updated>
|
||||
<workItem from="1683656474920" duration="401000" />
|
||||
</task>
|
||||
<servers />
|
||||
</component>
|
||||
<component name="TypeScriptGeneratedFilesManager">
|
||||
<option name="version" value="3" />
|
||||
</component>
|
||||
<component name="Vcs.Log.Tabs.Properties">
|
||||
<option name="TAB_STATES">
|
||||
<map>
|
||||
<entry key="MAIN">
|
||||
<value>
|
||||
<State />
|
||||
</value>
|
||||
</entry>
|
||||
</map>
|
||||
</option>
|
||||
</component>
|
||||
</project>
|
||||
Loading…
Reference in New Issue