├── README.md
├── dags
    └── surf_dag
    │   └── main.py
├── docker-compose.yaml
├── processed_data
    └── 05-12-22-surf-report.csv
├── raw_data
    ├── 05-12-22-surf-report.csv
    ├── 05-12-22surf-report.csv
    ├── 051222surf-report.csv
    ├── surf-report.csv
    ├── tides.csv
    └── waves.csv
└── surfdash.py


/README.md:
--------------------------------------------------------------------------------
 1 | # Surfline Dashboard
 2 | 
 3 | ## **Architecture**
 4 | 
 5 | 
 6 | <img width="4896" alt="Surfline App Architecture" src="https://user-images.githubusercontent.com/5299312/169568492-4ada773b-a77b-485e-9c2e-b4538017ef59.png">
 7 | 
 8 | 
 9 | ## **Overview**
10 | 
11 | The pipeline collects data from the surfline API and exports a csv file to S3. Then the most recent file in S3 is downloaded to be ingested into the Postgres datawarehouse. A temp table is created and then the unique rows are inserted into the data tables. Airflow is used for orchestration and hosted locally with docker-compose and mysql. Postgres is also running locally in a docker container. The data dashboard is run locally with ploty.
12 | 
13 | ## **ETL** 
14 | 
15 | ![image](https://user-images.githubusercontent.com/5299312/169564659-76d6cde9-fc59-4d18-9fc4-d8d6f8fa1c0b.png)
16 | 
17 | ## **Data Warehouse - Postgres**
18 | 
19 | ![image](https://user-images.githubusercontent.com/5299312/169566679-3d46d244-b139-4414-a406-c5c18d981ac3.png)
20 | 
21 | ## **Data Dashboard**
22 | 
23 | ![image](https://user-images.githubusercontent.com/5299312/169568656-e6a77014-4bd2-4d21-9236-f24d6f1061b7.png)
24 | 
25 | ## **Learning Resources**
26 | 
27 | Airflow Basics:
28 | 
29 | [Airflow DAG: Coding your first DAG for Beginners](https://www.youtube.com/watch?v=IH1-0hwFZRQ)
30 | 
31 | [Running Airflow 2.0 with Docker in 5 mins](https://www.youtube.com/watch?v=aTaytcxy2Ck)
32 | 
33 | S3 Basics:
34 | 
35 | [Setting Up Airflow Tasks To Connect Postgres And S3](https://www.youtube.com/watch?v=30VDVVSNLcc)
36 | 
37 | [How to Upload files to AWS S3 using Python and Boto3](https://www.youtube.com/watch?v=G68oSgFotZA)
38 | 
39 | [Download files from S3](https://www.stackvidhya.com/download-files-from-s3-using-boto3/)
40 | 
41 | Docker Basics:
42 | 
43 | [Docker Tutorial for Beginners](https://www.youtube.com/watch?v=3c-iBn73dDE)
44 | 
45 | [Docker and PostgreSQL](https://www.youtube.com/watch?v=aHbE3pTyG-Q)
46 | 
47 | [Build your first pipeline DAG | Apache airflow for beginners](https://www.youtube.com/watch?v=28UI_Usxbqo)
48 | 
49 | [Run Airflow 2.0 via Docker | Minimal Setup | Apache airflow for beginners](https://www.youtube.com/watch?v=TkvX1L__g3s&t=389s)
50 | 
51 | [Docker Network Bridge](https://docs.docker.com/network/bridge/)
52 | 
53 | [Docker Curriculum](https://docker-curriculum.com/)
54 | 
55 | [Docker Compose - Airflow](https://medium.com/@rajat.mca.du.2015/airflow-and-mysql-with-docker-containers-80ed9c2bd340)
56 | 
57 | Plotly:
58 | 
59 | [Introduction to Plotly](https://www.youtube.com/watch?v=hSPmj7mK6ng)
60 | 


--------------------------------------------------------------------------------
/dags/surf_dag/main.py:
--------------------------------------------------------------------------------
  1 | # We'll start by importing the DAG object
  2 | from datetime import timedelta
  3 | 
  4 | from airflow import DAG
  5 | # We need to import the operators used in our tasks
  6 | from airflow.operators.python_operator import PythonOperator
  7 | # We then import the days_ago function
  8 | from airflow.utils.dates import days_ago
  9 | 
 10 | import pandas as pd
 11 | import psycopg2
 12 | import os
 13 | from pysurfline import SurfReport
 14 | import boto3
 15 | from datetime import date
 16 | 
 17 | 
 18 | # get dag directory path
 19 | dag_path = os.getcwd()
 20 | 
 21 | 
 22 | def download_data():
 23 |     params = {
 24 |     "spotId": "5842041f4e65fad6a77087f9",
 25 |     "days": 1,
 26 |     "intervalHours": 1,}
 27 | 
 28 |     today = date.today()
 29 |     surfdate = today.strftime("%m-%d-%y")
 30 | 
 31 |     report = SurfReport(params)
 32 |     print(report.api_log)
 33 |     
 34 |     surf_report = report.df.drop(columns=['utcOffset','swells'])
 35 |     surf_report.to_csv(dag_path+'/raw_data/'+ surfdate +'-surf-report.csv',header=False)
 36 | 
 37 | 
 38 | def load_s3_data():
 39 |     today = date.today()
 40 |     surfdate = today.strftime("%m-%d-%y")
 41 |     session = boto3.Session(
 42 |     aws_access_key_id="",
 43 |     aws_secret_access_key="",)
 44 |     s3 = session.resource('s3')
 45 |     
 46 |     s3.meta.client.upload_file(dag_path+'/raw_data/'+ surfdate +'-surf-report.csv','wavestorm',surfdate +'-surf-report.csv')
 47 |     
 48 | 
 49 | def download_s3_data():
 50 | 
 51 |     s3 = boto3.client('s3',aws_access_key_id="",
 52 |     aws_secret_access_key="")
 53 | 
 54 |     get_last_modified = lambda obj: int(obj['LastModified'].strftime('%s'))
 55 | 
 56 |     objs = s3.list_objects_v2(Bucket='wavestorm')['Contents']
 57 |     last_added = [obj['Key'] for obj in sorted(objs, key=get_last_modified)][-1]
 58 | 
 59 |     session = boto3.Session(
 60 |     aws_access_key_id="",
 61 |     aws_secret_access_key="",)
 62 |     s3 = session.resource('s3')
 63 |     
 64 | 
 65 |     s3.Bucket('wavestorm').download_file(last_added,dag_path+'/processed_data/'+last_added)
 66 |     
 67 | 
 68 | def load_data():
 69 |     #establishing the connection
 70 |     conn = psycopg2.connect(
 71 |     database="storm", user='postgres', password='wavestorm', host='172.17.0.1', port= '5432'
 72 |     )
 73 |     #Creating a cursor object using the cursor() method
 74 |     cursor = conn.cursor()
 75 | 
 76 |     #Executing an MYSQL function using the execute() method
 77 |     cursor.execute("select version()")
 78 | 
 79 |     # Fetch a single row using fetchone() method.
 80 |     data = cursor.fetchone()
 81 |     print("Connection established to: ",data)
 82 | 
 83 |     s3 = boto3.client('s3',aws_access_key_id="",
 84 |     aws_secret_access_key="")
 85 | 
 86 |     get_last_modified = lambda obj: int(obj['LastModified'].strftime('%s'))
 87 | 
 88 |     objs = s3.list_objects_v2(Bucket='wavestorm')['Contents']
 89 |     last_added = [obj['Key'] for obj in sorted(objs, key=get_last_modified)][-1]
 90 | 
 91 |     command = (
 92 |         """
 93 | 
 94 |         CREATE TEMPORARY TABLE IF NOT EXISTS staging_surf_report (
 95 |             timestamp TIMESTAMP PRIMARY KEY,
 96 |             surf_min INTEGER,
 97 |             surf_max INTEGER,
 98 |             surf_optimalScore INTEGER,
 99 |             surf_plus BOOL,
100 |             surf_humanRelation VARCHAR(255),
101 |             surf_raw_min NUMERIC,
102 |             surf_raw_max NUMERIC,
103 |             speed NUMERIC,
104 |             direction NUMERIC,
105 |             directionType VARCHAR(255),
106 |             gust NUMERIC,
107 |             optimalScore INTEGER,
108 |             temperature NUMERIC,
109 |             condition VARCHAR(255)
110 |         );
111 |         """)
112 | 
113 | 
114 |     print(command)
115 |     cursor.execute(command)
116 |     conn.commit()
117 | 
118 | 
119 |     print(last_added)
120 |     f = open(dag_path+'/processed_data/'+last_added, 'r')
121 |     print(f)
122 |     try:
123 |         cursor.copy_from(f, 'staging_surf_report', sep=",")
124 |         print("Data inserted using copy_from_datafile() successfully....")
125 |     except (Exception, psycopg2.DatabaseError) as err:
126 |         #os.remove(dag_path+'/processed_data/'+last_added)
127 |             # pass exception to function
128 |         print(psycopg2.DatabaseError)
129 |         print(Exception)
130 |         show_psycopg2_exception(err)
131 |         cursor.close()
132 |     conn.commit()
133 | 
134 | 
135 |     command = ("""
136 | 
137 |         INSERT INTO surf_report
138 |       (timestamp, surf_min,surf_max,surf_optimalScore,surf_plus,surf_humanRelation,surf_raw_min,surf_raw_max,speed,direction,directionType,gust,optimalScore,temperature,condition)
139 |         SELECT *
140 |     FROM staging_surf_report
141 |         WHERE NOT EXISTS(SELECT timestamp
142 |                     FROM surf_report 
143 |                    WHERE staging_surf_report.timestamp = surf_report.timestamp);""")
144 | 
145 | 
146 |     cursor.execute(command)
147 |     conn.commit()
148 |     conn.close()
149 | 
150 | # initializing the default arguments that we'll pass to our DAG
151 | default_args = {
152 |     'owner': 'airflow',
153 |     'start_date': days_ago(5)
154 | }
155 | 
156 | ingestion_dag = DAG(
157 |     'surf_dag',
158 |     default_args=default_args,
159 |     description='Aggregates booking records for data analysis',
160 |     schedule_interval=timedelta(days=1),
161 |     catchup=False
162 | )
163 | 
164 | task_1 = PythonOperator(
165 |     task_id='download_data',
166 |     python_callable=download_data,
167 |     dag=ingestion_dag,
168 | )
169 | 
170 | task_2 = PythonOperator(
171 |     task_id='load_s3_data',
172 |     python_callable=load_s3_data,
173 |     dag=ingestion_dag,
174 | )
175 | 
176 | task_3 = PythonOperator(
177 |     task_id='download_s3_data',
178 |     python_callable=download_s3_data,
179 |     dag=ingestion_dag,
180 | )
181 | 
182 | 
183 | task_4 = PythonOperator(
184 |     task_id='load_data',
185 |     python_callable=load_data,
186 |     dag=ingestion_dag,
187 | )
188 | 
189 | #task_3
190 | task_1 >> task_2 >> task_3 >> task_4


--------------------------------------------------------------------------------
/docker-compose.yaml:
--------------------------------------------------------------------------------
  1 | # Licensed to the Apache Software Foundation (ASF) under one
  2 | # or more contributor license agreements.  See the NOTICE file
  3 | # distributed with this work for additional information
  4 | # regarding copyright ownership.  The ASF licenses this file
  5 | # to you under the Apache License, Version 2.0 (the
  6 | # "License"); you may not use this file except in compliance
  7 | # with the License.  You may obtain a copy of the License at
  8 | #
  9 | #   http://www.apache.org/licenses/LICENSE-2.0
 10 | #
 11 | # Unless required by applicable law or agreed to in writing,
 12 | # software distributed under the License is distributed on an
 13 | # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 14 | # KIND, either express or implied.  See the License for the
 15 | # specific language governing permissions and limitations
 16 | # under the License.
 17 | #
 18 | 
 19 | ---
 20 | version: '3'
 21 | 
 22 | x-airflow-common:
 23 |   &airflow-common
 24 |   image: apache/airflow:2.1.1-python3.8
 25 |   environment:
 26 |     &airflow-common-env
 27 |     AIRFLOW__CORE__SQL_ALCHEMY_CONN: mysql://airflow:airflow@mysql:3306/airflow
 28 |     AIRFLOW__CORE__LOAD_EXAMPLES: 'false'
 29 |     _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:- pysurfline}
 30 | 
 31 |   volumes:
 32 |     - ./dags:/opt/airflow/dags
 33 |     - ./logs:/opt/airflow/logs
 34 |     - ./raw_data:/opt/airflow/raw_data
 35 |     - ./processed_data:/opt/airflow/processed_data
 36 |     - ./plugins:/opt/airflow/plugins
 37 |   user: "${AIRFLOW_UID:-50000}:${AIRFLOW_GID:-50000}"
 38 |   depends_on:
 39 |     mysql:
 40 |       condition: service_healthy
 41 | 
 42 | services:
 43 |   mysql:
 44 |     image: mysql:5.7
 45 |     command: --explicit_defaults_for_timestamp
 46 |     ports:
 47 |       - "3306:3306"
 48 |     volumes:
 49 |       - my-db:/var/lib/mysql
 50 |     environment:
 51 |       MYSQL_ROOT_USER: airflow
 52 |       MYSQL_ROOT_PASSWORD: airflow
 53 |       MYSQL_USER: airflow
 54 |       MYSQL_PASSWORD: airflow
 55 |       MYSQL_DATABASE: airflow
 56 |     healthcheck:
 57 |       test: "/etc/init.d/mysql status"
 58 |       interval: 2s
 59 |       retries: 120
 60 | 
 61 |   airflow-init:
 62 |     <<: *airflow-common
 63 |     entrypoint: /bin/bash -c "/bin/bash -c \"$${@}\""
 64 |     command: |
 65 |       /bin/bash -c "
 66 |         airflow db init
 67 |         airflow db upgrade
 68 |         airflow users create -r Admin -u admin -e airflow@airflow.com -f admin -l user -p airflow
 69 |       "
 70 |     environment:
 71 |       <<: *airflow-common-env
 72 | 
 73 |   airflow-scheduler:
 74 |     <<: *airflow-common
 75 |     command: scheduler
 76 |     environment:
 77 |       <<: *airflow-common-env
 78 |     restart: always
 79 | 
 80 |   airflow-webserver:
 81 |     <<: *airflow-common
 82 |     command: webserver
 83 |     
 84 |     ports:
 85 |       - 8081:8080
 86 |     healthcheck:
 87 |       test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
 88 |       interval: 10s
 89 |       timeout: 10s
 90 |       retries: 5
 91 |     restart: always
 92 |     environment:
 93 |       <<: *airflow-common-env
 94 | 
 95 | networks:
 96 |   backend:
 97 |     driver: "bridge"
 98 | 
 99 | # Names our volume
100 | volumes:
101 |   my-db:


--------------------------------------------------------------------------------
/processed_data/05-12-22-surf-report.csv:
--------------------------------------------------------------------------------
1 | 2022-05-12 07:00:00,3,4,2,False,Waist to chest,3.28084,3.74016,12.31638,304.54605,Onshore,21.77107,0,50.36002,NIGHT_CLEAR
2 | 2022-05-12 10:00:00,2,3,2,True,Thigh to stomach,2.131885,3.12402,11.52399,303.42756,Onshore,22.93737,0,50.17996,NIGHT_CLEAR
3 | 2022-05-12 13:00:00,2,3,2,False,Thigh to waist,2.0,3.0,12.13841,306.29277,Onshore,16.71707,0,49.31599,CLEAR
4 | 2022-05-12 16:00:00,2,3,2,True,Thigh to stomach,2.0787400000000003,3.07874,12.30578,301.93971,Onshore,17.49461,0,48.00203,CLOUDY
5 | 2022-05-12 19:00:00,2,3,2,False,Thigh to waist,2.0,3.0,14.03576,299.88866,Onshore,18.07776,0,48.56002,MOSTLY_CLEAR
6 | 2022-05-12 22:00:00,2,3,0,False,Thigh to waist,1.9409480000000001,2.960634,14.67359,293.18491,Onshore,19.43845,0,49.29797,CLEAR
7 | 2022-05-13 01:00:00,2,3,2,False,Thigh to waist,2.0,3.0,15.55441,301.07205,Onshore,26.24191,0,50.53998,CLEAR
8 | 2022-05-13 04:00:00,2,3,2,True,Thigh to stomach,2.1811000000000003,3.1568199999999997,12.98632,309.05588,Onshore,25.65876,0,50.36002,NIGHT_CLEAR
9 | 


--------------------------------------------------------------------------------
/raw_data/05-12-22-surf-report.csv:
--------------------------------------------------------------------------------
1 | 2022-05-12 07:00:00,3,4,2,False,Waist to chest,3.28084,3.74016,12.31638,304.54605,Onshore,21.77107,0,50.36002,NIGHT_CLEAR
2 | 2022-05-12 10:00:00,2,3,2,True,Thigh to stomach,2.131885,3.12402,11.52399,303.42756,Onshore,22.93737,0,50.17996,NIGHT_CLEAR
3 | 2022-05-12 13:00:00,2,3,2,False,Thigh to waist,2.0,3.0,12.13841,306.29277,Onshore,16.71707,0,49.31599,CLEAR
4 | 2022-05-12 16:00:00,2,3,2,True,Thigh to stomach,2.0787400000000003,3.07874,12.30578,301.93971,Onshore,17.49461,0,48.00203,CLOUDY
5 | 2022-05-12 19:00:00,2,3,2,False,Thigh to waist,2.0,3.0,14.03576,299.88866,Onshore,18.07776,0,48.56002,MOSTLY_CLEAR
6 | 2022-05-12 22:00:00,2,3,0,False,Thigh to waist,1.9409480000000001,2.960634,14.67359,293.18491,Onshore,19.43845,0,49.29797,CLEAR
7 | 2022-05-13 01:00:00,2,3,2,False,Thigh to waist,2.0,3.0,15.55441,301.07205,Onshore,26.24191,0,50.53998,CLEAR
8 | 2022-05-13 04:00:00,2,3,2,True,Thigh to stomach,2.1811000000000003,3.1568199999999997,12.98632,309.05588,Onshore,25.65876,0,50.36002,NIGHT_CLEAR
9 | 


--------------------------------------------------------------------------------
/raw_data/05-12-22surf-report.csv:
--------------------------------------------------------------------------------
 1 | timestamp,utcOffset,surf_min,surf_max,surf_optimalScore,surf_plus,surf_humanRelation,surf_raw_min,surf_raw_max,swells,utcOffset,speed,direction,directionType,gust,optimalScore,utcOffset,temperature,condition
 2 | 2022-05-11 07:00:00,-7,4,5,2,False,Chest to head,4.33071,4.85564,"[{'height': 6.40682, 'period': 10, 'direction': 302.83807, 'directionMin': 291.112125, 'optimalScore': 0}, {'height': 1.62536, 'period': 13, 'direction': 201.36981, 'directionMin': 196.205475, 'optimalScore': 0}, {'height': 0.59469, 'period': 11, 'direction': 163.30792, 'directionMin': 159.731445, 'optimalScore': 0}, {'height': 0.31804, 'period': 19, 'direction': 210.29364, 'directionMin': 204.388295, 'optimalScore': 0}, {'height': 0.26119, 'period': 16, 'direction': 284.66412, 'directionMin': 280.69032000000004, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,10.02494,320.11238,Onshore,16.71707,0,-7,49.496,NIGHT_CLEAR
 3 | 2022-05-11 10:00:00,-7,4,6,2,False,Chest to overhead,3.977035,5.042649999999999,"[{'height': 6.25801, 'period': 10, 'direction': 302.98505, 'directionMin': 291.103995, 'optimalScore': 0}, {'height': 1.57992, 'period': 13, 'direction': 201.73944, 'directionMin': 196.45204, 'optimalScore': 0}, {'height': 0.56624, 'period': 11, 'direction': 163.09503, 'directionMin': 159.51381, 'optimalScore': 0}, {'height': 0.38123, 'period': 19, 'direction': 210.2182, 'directionMin': 203.17021499999998, 'optimalScore': 0}, {'height': 0.28176, 'period': 16, 'direction': 284.17993, 'directionMin': 280.31114, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,10.48745,320.91681,Onshore,16.52268,0,-7,48.84797,NIGHT_CLEAR
 4 | 2022-05-11 13:00:00,-7,3,5,2,False,Waist to head,3.0,5.0,"[{'height': 6.03179, 'period': 9, 'direction': 300.87885, 'directionMin': 289.87952, 'optimalScore': 0}, {'height': 1.61673, 'period': 13, 'direction': 201.97192, 'directionMin': 196.540445, 'optimalScore': 0}, {'height': 0.52618, 'period': 11, 'direction': 162.40509, 'directionMin': 159.051955, 'optimalScore': 0}, {'height': 0.38743, 'period': 19, 'direction': 212.64392, 'directionMin': 205.424225, 'optimalScore': 0}, {'height': 0.29308, 'period': 16, 'direction': 285.64435, 'directionMin': 281.523575, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,3.07598,346.22836,Cross-shore,6.2203,2,-7,48.16402,CLEAR
 5 | 2022-05-11 16:00:00,-7,3,4,2,True,Waist to shoulder,2.986882,4.006562000000001,"[{'height': 6.01742, 'period': 9, 'direction': 300.63123, 'directionMin': 290.34665, 'optimalScore': 0}, {'height': 1.63707, 'period': 13, 'direction': 201.77783, 'directionMin': 196.226775, 'optimalScore': 0}, {'height': 0.49465, 'period': 11, 'direction': 162.26929, 'directionMin': 158.8711, 'optimalScore': 0}, {'height': 0.30253, 'period': 19, 'direction': 222.07281, 'directionMin': 217.174115, 'optimalScore': 0}, {'height': 0.40453, 'period': 15, 'direction': 286.88751, 'directionMin': 283.067675, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,4.09031,281.21758,Onshore,4.66523,2,-7,47.46199,CLEAR
 6 | 2022-05-11 19:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 5.96053, 'period': 9, 'direction': 301.50568, 'directionMin': 291.25786, 'optimalScore': 0}, {'height': 1.61959, 'period': 13, 'direction': 201.64026, 'directionMin': 196.010805, 'optimalScore': 0}, {'height': 0.46575, 'period': 11, 'direction': 162.17615, 'directionMin': 158.773615, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}, {'height': 0.4939, 'period': 15, 'direction': 286.68591, 'directionMin': 282.90094999999997, 'optimalScore': 0}, {'height': 0.20512, 'period': 16, 'direction': 233.78076, 'directionMin': 231.22822499999998, 'optimalScore': 0}]",-7,14.08923,296.92239,Onshore,17.88338,0,-7,48.97399,CLEAR
 7 | 2022-05-11 22:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 6.41096, 'period': 9, 'direction': 304.17798, 'directionMin': 293.099495, 'optimalScore': 0}, {'height': 1.56847, 'period': 13, 'direction': 200.98853, 'directionMin': 195.473165, 'optimalScore': 0}, {'height': 0.43786, 'period': 11, 'direction': 162.18292, 'directionMin': 158.72098, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}, {'height': 0.49478, 'period': 15, 'direction': 286.41113, 'directionMin': 282.741065, 'optimalScore': 0}, {'height': 0.22703, 'period': 16, 'direction': 233.96613, 'directionMin': 231.31288, 'optimalScore': 0}]",-7,19.22053,304.648,Onshore,26.82506,0,-7,49.29797,CLEAR
 8 | 2022-05-12 01:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 6.73481, 'period': 9, 'direction': 306.15045, 'directionMin': 294.392065, 'optimalScore': 0}, {'height': 1.45656, 'period': 13, 'direction': 199.93683, 'directionMin': 194.57262999999998, 'optimalScore': 0}, {'height': 0.41066, 'period': 11, 'direction': 162.2345, 'directionMin': 158.72821, 'optimalScore': 0}, {'height': 0.41171, 'period': 18, 'direction': 205.45667, 'directionMin': 200.92344, 'optimalScore': 0}, {'height': 0.42782, 'period': 15, 'direction': 285.66772, 'directionMin': 281.988205, 'optimalScore': 0}, {'height': 0.21837, 'period': 16, 'direction': 234.21588, 'directionMin': 231.396045, 'optimalScore': 0}]",-7,19.86497,308.08838,Onshore,29.15768,0,-7,49.51402,CLEAR
 9 | 2022-05-12 04:00:00,-7,3,4,2,True,Waist to shoulder,3.091205,4.066929999999999,"[{'height': 6.6271, 'period': 7, 'direction': 307.7179, 'directionMin': 295.63340999999997, 'optimalScore': 0}, {'height': 1.3435, 'period': 13, 'direction': 200.63428, 'directionMin': 195.42863, 'optimalScore': 0}, {'height': 0.38097, 'period': 11, 'direction': 161.66565, 'directionMin': 158.446305, 'optimalScore': 0}, {'height': 0.59278, 'period': 18, 'direction': 196.81744, 'directionMin': 191.00425, 'optimalScore': 0}, {'height': 0.47575, 'period': 14, 'direction': 287.34473, 'directionMin': 283.49074, 'optimalScore': 0}, {'height': 0.19219, 'period': 16, 'direction': 234.17261, 'directionMin': 231.41558, 'optimalScore': 0}]",-7,16.45581,309.3965,Onshore,24.10368,0,-7,48.43401,NIGHT_CLEAR
10 | 


--------------------------------------------------------------------------------
/raw_data/051222surf-report.csv:
--------------------------------------------------------------------------------
 1 | timestamp,utcOffset,surf_min,surf_max,surf_optimalScore,surf_plus,surf_humanRelation,surf_raw_min,surf_raw_max,swells,utcOffset,speed,direction,directionType,gust,optimalScore,utcOffset,temperature,condition
 2 | 2022-05-11 07:00:00,-7,4,5,2,False,Chest to head,4.33071,4.85564,"[{'height': 6.40682, 'period': 10, 'direction': 302.83807, 'directionMin': 291.112125, 'optimalScore': 0}, {'height': 1.62536, 'period': 13, 'direction': 201.36981, 'directionMin': 196.205475, 'optimalScore': 0}, {'height': 0.59469, 'period': 11, 'direction': 163.30792, 'directionMin': 159.731445, 'optimalScore': 0}, {'height': 0.31804, 'period': 19, 'direction': 210.29364, 'directionMin': 204.388295, 'optimalScore': 0}, {'height': 0.26119, 'period': 16, 'direction': 284.66412, 'directionMin': 280.69032000000004, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,10.02494,320.11238,Onshore,16.71707,0,-7,49.496,NIGHT_CLEAR
 3 | 2022-05-11 10:00:00,-7,4,6,2,False,Chest to overhead,3.977035,5.042649999999999,"[{'height': 6.25801, 'period': 10, 'direction': 302.98505, 'directionMin': 291.103995, 'optimalScore': 0}, {'height': 1.57992, 'period': 13, 'direction': 201.73944, 'directionMin': 196.45204, 'optimalScore': 0}, {'height': 0.56624, 'period': 11, 'direction': 163.09503, 'directionMin': 159.51381, 'optimalScore': 0}, {'height': 0.38123, 'period': 19, 'direction': 210.2182, 'directionMin': 203.17021499999998, 'optimalScore': 0}, {'height': 0.28176, 'period': 16, 'direction': 284.17993, 'directionMin': 280.31114, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,10.48745,320.91681,Onshore,16.52268,0,-7,48.84797,NIGHT_CLEAR
 4 | 2022-05-11 13:00:00,-7,3,5,2,False,Waist to head,3.0,5.0,"[{'height': 6.03179, 'period': 9, 'direction': 300.87885, 'directionMin': 289.87952, 'optimalScore': 0}, {'height': 1.61673, 'period': 13, 'direction': 201.97192, 'directionMin': 196.540445, 'optimalScore': 0}, {'height': 0.52618, 'period': 11, 'direction': 162.40509, 'directionMin': 159.051955, 'optimalScore': 0}, {'height': 0.38743, 'period': 19, 'direction': 212.64392, 'directionMin': 205.424225, 'optimalScore': 0}, {'height': 0.29308, 'period': 16, 'direction': 285.64435, 'directionMin': 281.523575, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,3.07598,346.22836,Cross-shore,6.2203,2,-7,48.16402,CLEAR
 5 | 2022-05-11 16:00:00,-7,3,4,2,True,Waist to shoulder,2.986882,4.006562000000001,"[{'height': 6.01742, 'period': 9, 'direction': 300.63123, 'directionMin': 290.34665, 'optimalScore': 0}, {'height': 1.63707, 'period': 13, 'direction': 201.77783, 'directionMin': 196.226775, 'optimalScore': 0}, {'height': 0.49465, 'period': 11, 'direction': 162.26929, 'directionMin': 158.8711, 'optimalScore': 0}, {'height': 0.30253, 'period': 19, 'direction': 222.07281, 'directionMin': 217.174115, 'optimalScore': 0}, {'height': 0.40453, 'period': 15, 'direction': 286.88751, 'directionMin': 283.067675, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,4.09031,281.21758,Onshore,4.66523,2,-7,47.46199,CLEAR
 6 | 2022-05-11 19:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 5.96053, 'period': 9, 'direction': 301.50568, 'directionMin': 291.25786, 'optimalScore': 0}, {'height': 1.61959, 'period': 13, 'direction': 201.64026, 'directionMin': 196.010805, 'optimalScore': 0}, {'height': 0.46575, 'period': 11, 'direction': 162.17615, 'directionMin': 158.773615, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}, {'height': 0.4939, 'period': 15, 'direction': 286.68591, 'directionMin': 282.90094999999997, 'optimalScore': 0}, {'height': 0.20512, 'period': 16, 'direction': 233.78076, 'directionMin': 231.22822499999998, 'optimalScore': 0}]",-7,14.08923,296.92239,Onshore,17.88338,0,-7,48.97399,CLEAR
 7 | 2022-05-11 22:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 6.41096, 'period': 9, 'direction': 304.17798, 'directionMin': 293.099495, 'optimalScore': 0}, {'height': 1.56847, 'period': 13, 'direction': 200.98853, 'directionMin': 195.473165, 'optimalScore': 0}, {'height': 0.43786, 'period': 11, 'direction': 162.18292, 'directionMin': 158.72098, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}, {'height': 0.49478, 'period': 15, 'direction': 286.41113, 'directionMin': 282.741065, 'optimalScore': 0}, {'height': 0.22703, 'period': 16, 'direction': 233.96613, 'directionMin': 231.31288, 'optimalScore': 0}]",-7,19.22053,304.648,Onshore,26.82506,0,-7,49.29797,CLEAR
 8 | 2022-05-12 01:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 6.73481, 'period': 9, 'direction': 306.15045, 'directionMin': 294.392065, 'optimalScore': 0}, {'height': 1.45656, 'period': 13, 'direction': 199.93683, 'directionMin': 194.57262999999998, 'optimalScore': 0}, {'height': 0.41066, 'period': 11, 'direction': 162.2345, 'directionMin': 158.72821, 'optimalScore': 0}, {'height': 0.41171, 'period': 18, 'direction': 205.45667, 'directionMin': 200.92344, 'optimalScore': 0}, {'height': 0.42782, 'period': 15, 'direction': 285.66772, 'directionMin': 281.988205, 'optimalScore': 0}, {'height': 0.21837, 'period': 16, 'direction': 234.21588, 'directionMin': 231.396045, 'optimalScore': 0}]",-7,19.86497,308.08838,Onshore,29.15768,0,-7,49.51402,CLEAR
 9 | 2022-05-12 04:00:00,-7,3,4,2,True,Waist to shoulder,3.091205,4.066929999999999,"[{'height': 6.6271, 'period': 7, 'direction': 307.7179, 'directionMin': 295.63340999999997, 'optimalScore': 0}, {'height': 1.3435, 'period': 13, 'direction': 200.63428, 'directionMin': 195.42863, 'optimalScore': 0}, {'height': 0.38097, 'period': 11, 'direction': 161.66565, 'directionMin': 158.446305, 'optimalScore': 0}, {'height': 0.59278, 'period': 18, 'direction': 196.81744, 'directionMin': 191.00425, 'optimalScore': 0}, {'height': 0.47575, 'period': 14, 'direction': 287.34473, 'directionMin': 283.49074, 'optimalScore': 0}, {'height': 0.19219, 'period': 16, 'direction': 234.17261, 'directionMin': 231.41558, 'optimalScore': 0}]",-7,16.45581,309.3965,Onshore,24.10368,0,-7,48.43401,NIGHT_CLEAR
10 | 


--------------------------------------------------------------------------------
/raw_data/surf-report.csv:
--------------------------------------------------------------------------------
 1 | timestamp,utcOffset,surf_min,surf_max,surf_optimalScore,surf_plus,surf_humanRelation,surf_raw_min,surf_raw_max,swells,utcOffset,speed,direction,directionType,gust,optimalScore,utcOffset,temperature,condition
 2 | 2022-05-11 07:00:00,-7,4,5,2,False,Chest to head,4.33071,4.85564,"[{'height': 6.40682, 'period': 10, 'direction': 302.83807, 'directionMin': 291.112125, 'optimalScore': 0}, {'height': 1.62536, 'period': 13, 'direction': 201.36981, 'directionMin': 196.205475, 'optimalScore': 0}, {'height': 0.59469, 'period': 11, 'direction': 163.30792, 'directionMin': 159.731445, 'optimalScore': 0}, {'height': 0.31804, 'period': 19, 'direction': 210.29364, 'directionMin': 204.388295, 'optimalScore': 0}, {'height': 0.26119, 'period': 16, 'direction': 284.66412, 'directionMin': 280.69032000000004, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,10.02494,320.11238,Onshore,16.71707,0,-7,49.496,NIGHT_CLEAR
 3 | 2022-05-11 10:00:00,-7,4,6,2,False,Chest to overhead,3.977035,5.042649999999999,"[{'height': 6.25801, 'period': 10, 'direction': 302.98505, 'directionMin': 291.103995, 'optimalScore': 0}, {'height': 1.57992, 'period': 13, 'direction': 201.73944, 'directionMin': 196.45204, 'optimalScore': 0}, {'height': 0.56624, 'period': 11, 'direction': 163.09503, 'directionMin': 159.51381, 'optimalScore': 0}, {'height': 0.38123, 'period': 19, 'direction': 210.2182, 'directionMin': 203.17021499999998, 'optimalScore': 0}, {'height': 0.28176, 'period': 16, 'direction': 284.17993, 'directionMin': 280.31114, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,10.48745,320.91681,Onshore,16.52268,0,-7,48.84797,NIGHT_CLEAR
 4 | 2022-05-11 13:00:00,-7,3,5,2,False,Waist to head,3.0,5.0,"[{'height': 6.03179, 'period': 9, 'direction': 300.87885, 'directionMin': 289.87952, 'optimalScore': 0}, {'height': 1.61673, 'period': 13, 'direction': 201.97192, 'directionMin': 196.540445, 'optimalScore': 0}, {'height': 0.52618, 'period': 11, 'direction': 162.40509, 'directionMin': 159.051955, 'optimalScore': 0}, {'height': 0.38743, 'period': 19, 'direction': 212.64392, 'directionMin': 205.424225, 'optimalScore': 0}, {'height': 0.29308, 'period': 16, 'direction': 285.64435, 'directionMin': 281.523575, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,3.07598,346.22836,Cross-shore,6.2203,2,-7,48.16402,CLEAR
 5 | 2022-05-11 16:00:00,-7,3,4,2,True,Waist to shoulder,2.986882,4.006562000000001,"[{'height': 6.01742, 'period': 9, 'direction': 300.63123, 'directionMin': 290.34665, 'optimalScore': 0}, {'height': 1.63707, 'period': 13, 'direction': 201.77783, 'directionMin': 196.226775, 'optimalScore': 0}, {'height': 0.49465, 'period': 11, 'direction': 162.26929, 'directionMin': 158.8711, 'optimalScore': 0}, {'height': 0.30253, 'period': 19, 'direction': 222.07281, 'directionMin': 217.174115, 'optimalScore': 0}, {'height': 0.40453, 'period': 15, 'direction': 286.88751, 'directionMin': 283.067675, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}]",-7,4.09031,281.21758,Onshore,4.66523,2,-7,47.46199,CLEAR
 6 | 2022-05-11 19:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 5.96053, 'period': 9, 'direction': 301.50568, 'directionMin': 291.25786, 'optimalScore': 0}, {'height': 1.61959, 'period': 13, 'direction': 201.64026, 'directionMin': 196.010805, 'optimalScore': 0}, {'height': 0.46575, 'period': 11, 'direction': 162.17615, 'directionMin': 158.773615, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}, {'height': 0.4939, 'period': 15, 'direction': 286.68591, 'directionMin': 282.90094999999997, 'optimalScore': 0}, {'height': 0.20512, 'period': 16, 'direction': 233.78076, 'directionMin': 231.22822499999998, 'optimalScore': 0}]",-7,14.08923,296.92239,Onshore,17.88338,0,-7,48.97399,CLEAR
 7 | 2022-05-11 22:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 6.41096, 'period': 9, 'direction': 304.17798, 'directionMin': 293.099495, 'optimalScore': 0}, {'height': 1.56847, 'period': 13, 'direction': 200.98853, 'directionMin': 195.473165, 'optimalScore': 0}, {'height': 0.43786, 'period': 11, 'direction': 162.18292, 'directionMin': 158.72098, 'optimalScore': 0}, {'height': 0, 'period': 0, 'direction': 0, 'directionMin': 0, 'optimalScore': 0}, {'height': 0.49478, 'period': 15, 'direction': 286.41113, 'directionMin': 282.741065, 'optimalScore': 0}, {'height': 0.22703, 'period': 16, 'direction': 233.96613, 'directionMin': 231.31288, 'optimalScore': 0}]",-7,19.22053,304.648,Onshore,26.82506,0,-7,49.29797,CLEAR
 8 | 2022-05-12 01:00:00,-7,3,4,2,False,Waist to chest,3.0,4.0,"[{'height': 6.73481, 'period': 9, 'direction': 306.15045, 'directionMin': 294.392065, 'optimalScore': 0}, {'height': 1.45656, 'period': 13, 'direction': 199.93683, 'directionMin': 194.57262999999998, 'optimalScore': 0}, {'height': 0.41066, 'period': 11, 'direction': 162.2345, 'directionMin': 158.72821, 'optimalScore': 0}, {'height': 0.41171, 'period': 18, 'direction': 205.45667, 'directionMin': 200.92344, 'optimalScore': 0}, {'height': 0.42782, 'period': 15, 'direction': 285.66772, 'directionMin': 281.988205, 'optimalScore': 0}, {'height': 0.21837, 'period': 16, 'direction': 234.21588, 'directionMin': 231.396045, 'optimalScore': 0}]",-7,19.86497,308.08838,Onshore,29.15768,0,-7,49.51402,CLEAR
 9 | 2022-05-12 04:00:00,-7,3,4,2,True,Waist to shoulder,3.091205,4.066929999999999,"[{'height': 6.6271, 'period': 7, 'direction': 307.7179, 'directionMin': 295.63340999999997, 'optimalScore': 0}, {'height': 1.3435, 'period': 13, 'direction': 200.63428, 'directionMin': 195.42863, 'optimalScore': 0}, {'height': 0.38097, 'period': 11, 'direction': 161.66565, 'directionMin': 158.446305, 'optimalScore': 0}, {'height': 0.59278, 'period': 18, 'direction': 196.81744, 'directionMin': 191.00425, 'optimalScore': 0}, {'height': 0.47575, 'period': 14, 'direction': 287.34473, 'directionMin': 283.49074, 'optimalScore': 0}, {'height': 0.19219, 'period': 16, 'direction': 234.17261, 'directionMin': 231.41558, 'optimalScore': 0}]",-7,16.45581,309.3965,Onshore,24.10368,0,-7,48.43401,NIGHT_CLEAR
10 | 


--------------------------------------------------------------------------------
/raw_data/tides.csv:
--------------------------------------------------------------------------------
 1 | 2022-05-01 07:00:00,NORMAL,5.61
 2 | 2022-05-01 08:00:00,NORMAL,4.9
 3 | 2022-05-01 09:00:00,NORMAL,3.65
 4 | 2022-05-01 10:00:00,NORMAL,2.13
 5 | 2022-05-01 11:00:00,NORMAL,0.72
 6 | 2022-05-01 12:00:00,NORMAL,-0.23
 7 | 2022-05-01 12:59:17,LOW,-0.56
 8 | 2022-05-01 13:00:00,NORMAL,-0.55
 9 | 2022-05-01 14:00:00,NORMAL,-0.25
10 | 2022-05-01 15:00:00,NORMAL,0.52
11 | 2022-05-01 16:00:00,NORMAL,1.55
12 | 2022-05-01 17:00:00,NORMAL,2.63
13 | 2022-05-01 18:00:00,NORMAL,3.57
14 | 2022-05-01 19:00:00,NORMAL,4.2
15 | 2022-05-01 19:54:39,HIGH,4.4
16 | 2022-05-01 20:00:00,NORMAL,4.4
17 | 2022-05-01 21:00:00,NORMAL,4.13
18 | 2022-05-01 22:00:00,NORMAL,3.53
19 | 2022-05-01 23:00:00,NORMAL,2.84
20 | 2022-05-02 00:00:00,NORMAL,2.33
21 | 2022-05-02 00:47:55,LOW,2.2
22 | 2022-05-02 01:00:00,NORMAL,2.19
23 | 2022-05-02 02:00:00,NORMAL,2.5
24 | 2022-05-02 03:00:00,NORMAL,3.17
25 | 2022-05-02 04:00:00,NORMAL,4.03
26 | 2022-05-02 05:00:00,NORMAL,4.88
27 | 2022-05-02 06:00:00,NORMAL,5.5
28 | 2022-05-02 06:50:41,HIGH,5.68
29 | 2022-05-02 07:00:00,NORMAL,5.68
30 | 


--------------------------------------------------------------------------------
/raw_data/waves.csv:
--------------------------------------------------------------------------------
1 | Waves,Winder
2 | 5,10
3 | 15,20


--------------------------------------------------------------------------------
/surfdash.py:
--------------------------------------------------------------------------------
  1 | import pandas as pd
  2 | import plotly.express as px  # (version 4.7.0 or higher)
  3 | import plotly.graph_objects as go
  4 | from dash import Dash, dcc, html, Input, Output  # pip install dash (version 2.0.0 or higher)
  5 | from datetime import date
  6 | import datetime
  7 | import psycopg2
  8 | import os
  9 | 
 10 | #establishing the connection
 11 | conn = psycopg2.connect(
 12 |    database="storm", user='postgres', password='wavestorm', host='127.0.0.1', port= '5432'
 13 | )
 14 | #Creating a cursor object using the cursor() method
 15 | cursor = conn.cursor()
 16 | 
 17 | #Executing an MYSQL function using the execute() method
 18 | cursor.execute("select version()")
 19 | 
 20 | # Fetch a single row using fetchone() method.
 21 | data = cursor.fetchone()
 22 | print("Connection established to: ",data)
 23 | 
 24 | command = ("""select * from surf_report;""")
 25 | 
 26 | print(command)
 27 | cursor.execute(command)
 28 | conn.commit()
 29 | 
 30 | surf_report = cursor.fetchall()
 31 | conn.commit()
 32 | 
 33 | columns =  ['timestamp' ,
 34 |             'surf_min' ,
 35 |             'surf_max' ,
 36 |             'surf_optimalScore' ,
 37 |             'surf_plus' ,
 38 |             'surf_humanRelation' ,
 39 |             'surf_raw_min' ,
 40 |             'surf_raw_max' ,
 41 |             'speed',
 42 |             'direction',
 43 |             'directionType',
 44 |             'gust',
 45 |             'optimalScore',
 46 |             'temperature',
 47 |             'condition']
 48 | 
 49 | surf = pd.DataFrame(surf_report,columns=columns)
 50 | 
 51 | print(surf.head())
 52 | 
 53 | command = ("""select * from tides;""")
 54 | 
 55 | print(command)
 56 | cursor.execute(command)
 57 | conn.commit()
 58 | 
 59 | tides = cursor.fetchall()
 60 | conn.commit()
 61 | 
 62 | columns_tide =  ['timestamp' ,'tide','height']
 63 | 
 64 | tides = pd.DataFrame(tides,columns=columns_tide)
 65 | 
 66 | app = Dash(__name__)
 67 | 
 68 | # -- Import and clean data (importing csv into pandas)
 69 | # df = pd.read_csv("intro_bees.csv")
 70 | #df = pd.read_csv("https://raw.githubusercontent.com/Coding-with-Adam/Dash-by-Plotly/master/Other/Dash_Introduction/intro_bees.csv")
 71 | 
 72 | #df = df.groupby(['State', 'ANSI', 'Affected by', 'Year', 'state_code'])[['Pct of Colonies Impacted']].mean()
 73 | #df.reset_index(inplace=True)
 74 | print(surf[:5])
 75 | today = date.today()
 76 | date_list = [today - datetime.timedelta(days=x) for x in range(8)]
 77 | # ------------------------------------------------------------------------------
 78 | # App layout
 79 | app.layout = html.Div([
 80 | 
 81 |     html.H1("Ocean Beach Surf Report", style={'text-align': 'center'}),
 82 | 
 83 | 
 84 |     dcc.Dropdown(id="slct_year",
 85 |                  options=[
 86 |                      {"label": date_list[0], "value": date_list[0]},
 87 |                      {"label": date_list[1], "value": date_list[1]},
 88 |                      {"label": date_list[2], "value": date_list[2]},
 89 |                      {"label": date_list[3], "value": date_list[3]},
 90 |                      {"label": date_list[3], "value": date_list[3]},
 91 |                      {"label": date_list[4], "value": date_list[4]},
 92 |                      {"label": date_list[5], "value": date_list[5]},
 93 |                      {"label": date_list[6], "value": date_list[6]},
 94 |                      {"label": date_list[7], "value": date_list[7]}],
 95 |                  multi=False,
 96 |                  value=date_list[0],
 97 |                  style={'width': "40%"}
 98 |                  ),
 99 | 
100 |     html.Div(id='output_container', children=[]),
101 |     html.Br(),
102 | 
103 |     dcc.Graph(id='my_bee_map', figure={}),
104 |     dcc.Graph(id='my_bee_map2', figure={}),
105 |     dcc.Graph(id='my_bee_map3', figure={})
106 | 
107 | ])
108 | 
109 | 
110 | # ------------------------------------------------------------------------------
111 | # Connect the Plotly graphs with Dash Components
112 | @app.callback(
113 |     [Output(component_id='output_container', component_property='children'),
114 |      Output(component_id='my_bee_map', component_property='figure'),
115 |      Output(component_id='my_bee_map2', component_property='figure'),
116 |      Output(component_id='my_bee_map3', component_property='figure'),],
117 |     [Input(component_id='slct_year', component_property='value')]
118 | )
119 | def update_graph(option_slctd):
120 |     print(option_slctd)
121 |     print(type(option_slctd))
122 | 
123 |     if option_slctd  is None: 
124 |         container = "No Date selected"
125 |         option_slctd = str(date_list[0])
126 |         container = "Date selected: {}".format(option_slctd)
127 |     else:
128 |         container = "Date selected: {}".format(option_slctd)
129 |     
130 | 
131 |     print(type(option_slctd))
132 |     surff = surf.copy()
133 |     surff = surff[(surff["timestamp"] > datetime.datetime.strptime(option_slctd, "%Y-%m-%d")) & (surff["timestamp"] <= datetime.datetime.strptime(option_slctd, "%Y-%m-%d")+ datetime.timedelta(days=1))]  
134 |     tidess = tides.copy()
135 |     tidess = tidess[(tidess["timestamp"] > datetime.datetime.strptime(option_slctd, "%Y-%m-%d")) & (tidess["timestamp"] <= datetime.datetime.strptime(option_slctd, "%Y-%m-%d")+ datetime.timedelta(days=1))]  
136 |     # Plotly Express
137 |     fig = px.bar(surff, x='timestamp', y=['surf_min','surf_max'], text="surf_humanRelation",color_discrete_map={
138 |                 "surf_min": "lightsalmon",
139 |                 "surf_max": "indianred"},barmode='group')
140 | 
141 |     fig2 = px.scatter(surff, x="timestamp", y="speed",text="directionType",hover_data=["direction","timestamp","speed"])
142 |     fig2.update_traces(textposition='top center')
143 |     #fig2.add_annotation(text="directionType", x="timestamp", y="speed", arrowhead=1, showarrow=True,ax="direction")
144 |     #fig2.update_layout()
145 |     fig2.update_traces(marker=dict(size=12,
146 |                               line=dict(width=2,
147 |                                         color='DarkSlateGrey')),
148 |                     marker_colorbar_tickangle = 110,
149 |                     marker_symbol='arrow-right',
150 |                   selector=dict(mode='markers'))
151 |     fig3 = px.line(tidess,x="timestamp",y="height",text="tide")
152 |     fig3.update_traces(textposition='top center')
153 |     return container, fig, fig2, fig3
154 | 
155 | 
156 | # ------------------------------------------------------------------------------
157 | if __name__ == '__main__':
158 |     app.run_server(debug=True)


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