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107
daily_feature_power.py
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107
daily_feature_power.py
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import sys
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from pyspark.sql import SparkSession
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from pyspark.sql.functions import col, sum as _sum, hour, avg
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import happybase
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import matplotlib.pyplot as plt
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def convert_date(date_string):
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return f"{date_string[:4]}-{date_string[4:6]}-{date_string[6:]}"
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def convert_time(time_string):
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return f"{time_string[:2]}:{time_string[2:4]}:{time_string[4:]}"
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def get_region_code(dateline):
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year = dateline[:4]
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region_num = (int(year) - 2006) % 6
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return str(region_num)
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def get_double_rowkey(input_date):
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split = "_"
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start_datetime = input_date + "000000"
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stop_datetime = input_date + "235900"
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start_region_code = get_region_code(start_datetime)
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stop_region_code = get_region_code(stop_datetime)
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return start_region_code + split + start_datetime, stop_region_code + split + stop_datetime
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spark = SparkSession.builder \
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.appName("HBaseSparkIntegration") \
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.getOrCreate()
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hbase_host = "100.64.0.3"
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hbase_table = "elec:eleclog"
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connection = happybase.Connection(hbase_host)
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table = connection.table(hbase_table)
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column_family = "info"
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input_date = input("input date (20061217):")
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# input_date = "20061230"
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start_row, stop_row = get_double_rowkey(input_date)
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def fetch_hbase_data(table):
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none_num = 0
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rows = []
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for key, data in table.scan(row_start=start_row.encode("utf-8"), row_stop=stop_row.encode("utf-8")):
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date = data[f'{column_family}:date'.encode('utf-8')].decode('utf-8')
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time = data[f'{column_family}:time'.encode('utf-8')].decode('utf-8')
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value = data[f'{column_family}:globalActivePower'.encode('utf-8')].decode('utf-8')
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if value == "?":
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none_num += 1
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continue
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global_active_power = float(value)
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global_reactive_power = float(data[f'{column_family}:globalReactivePower'.encode('utf-8')].decode('utf-8'))
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voltage = float(data[f'{column_family}:voltage'.encode('utf-8')].decode('utf-8'))
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global_intensity = float(data[f'{column_family}:globalIntensity'.encode('utf-8')].decode('utf-8'))
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datetime = f"{convert_date(date)} {convert_time(time)}"
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rows.append((datetime, global_active_power, global_reactive_power, voltage, global_intensity))
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return rows, none_num
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hbase_data, none_num = fetch_hbase_data(table)
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if len(hbase_data) == 0:
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print("No data searched, please confirm your input")
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sys.exit(0)
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if none_num / len(hbase_data) >= 0.20:
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print("This batch data has too many nulls to be of analytical value ")
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sys.exit(0)
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columns = ["datetime", "global_active_power", "global_reactive_power", "voltage", "global_intensity"]
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df = spark.createDataFrame(hbase_data, columns)
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df = df.withColumn("hour", hour(col("datetime")))
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hourly_avg_features = df.groupBy("hour").agg(
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avg("global_active_power").alias("avg_global_active_power"),
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avg("global_reactive_power").alias("avg_global_reactive_power"),
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avg("voltage").alias("avg_voltage"),
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avg("global_intensity").alias("avg_global_intensity")
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)
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hourly_avg_features_pd = hourly_avg_features.orderBy("hour").toPandas()
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plt.figure(figsize=(12, 8))
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plt.plot(hourly_avg_features_pd['hour'], hourly_avg_features_pd['avg_global_active_power'], marker='o', label='Avg Global Active Power')
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plt.plot(hourly_avg_features_pd['hour'], hourly_avg_features_pd['avg_global_reactive_power'], marker='o', label='Avg Global Reactive Power')
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plt.plot(hourly_avg_features_pd['hour'], hourly_avg_features_pd['avg_voltage'], marker='o', label='Avg Voltage')
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plt.plot(hourly_avg_features_pd['hour'], hourly_avg_features_pd['avg_global_intensity'], marker='o', label='Avg Global Intensity')
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plt.title(f'Hourly Average Feature Values for {input_date}')
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plt.xlabel('Hour of the Day')
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plt.ylabel('Average Value')
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plt.legend()
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plt.grid(True)
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plt.xticks(range(24))
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plt.tight_layout()
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plt.show()
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spark.stop()
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109
daily_total_power.py
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109
daily_total_power.py
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import sys
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from datetime import datetime, timedelta
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from pyspark.sql import SparkSession
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from pyspark.sql.functions import col, sum as _sum, dayofmonth, date_format
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import happybase
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import matplotlib.pyplot as plt
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def convert_date(date_string):
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return f"{date_string[:4]}-{date_string[4:6]}-{date_string[6:]}"
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def convert_time(time_string):
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return f"{time_string[:2]}:{time_string[2:4]}:{time_string[4:]}"
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def get_region_code(dateline):
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year = dateline[:4]
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region_num = (int(year) - 2006) % 6
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return str(region_num)
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def get_week_dates(input_date):
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date = datetime.strptime(input_date, "%Y%m%d")
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start_of_week = date - timedelta(days=date.weekday())
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end_of_week = start_of_week + timedelta(days=6)
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start_of_week_str = start_of_week.strftime("%Y%m%d")
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end_of_week_str = end_of_week.strftime("%Y%m%d")
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return start_of_week_str, end_of_week_str
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def get_double_rowkey(input_date):
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split = "_"
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start_date, stop_date = get_week_dates(input_date)
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start_datetime = start_date + "000000"
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stop_datetime = stop_date + "235900"
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start_region_code = get_region_code(start_datetime)
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stop_region_code = get_region_code(stop_datetime)
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return start_region_code + split + start_datetime, stop_region_code + split + stop_datetime
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spark = SparkSession.builder \
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.appName("HBaseSparkIntegration") \
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.getOrCreate()
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hbase_host = "100.64.0.3"
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hbase_table = "elec:eleclog"
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connection = happybase.Connection(hbase_host)
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table = connection.table(hbase_table)
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column_family = "info"
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input_date = input("input date (20061217):")
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# input_date = "20061230"
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start_row, stop_row = get_double_rowkey(input_date)
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print(start_row, stop_row)
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def fetch_hbase_data(table):
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none_num = 0
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rows = []
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for key, data in table.scan(row_start=start_row.encode("utf-8"), row_stop=stop_row.encode("utf-8")):
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date = data[f'{column_family}:date'.encode('utf-8')].decode('utf-8')
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time = data[f'{column_family}:time'.encode('utf-8')].decode('utf-8')
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value = data[f'{column_family}:globalActivePower'.encode('utf-8')].decode('utf-8')
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if value == "?":
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none_num += 1
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continue
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global_active_power = float(value)
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datetime = f"{convert_date(date)} {convert_time(time)}"
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rows.append((datetime, global_active_power))
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return rows, none_num
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hbase_data, none_num = fetch_hbase_data(table)
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if len(hbase_data) == 0:
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print("No data searched, please confirm your input")
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sys.exit(0)
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if none_num / len(hbase_data) >= 0.20:
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print("This batch data has too many nulls to be of analytical value ")
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sys.exit(0)
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columns = ["datetime", "globalActivePower"]
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df = spark.createDataFrame(hbase_data, columns)
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df = df.withColumn("date", date_format(col("datetime"), "yyyy-MM-dd"))
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daily_total_power = df.groupBy("date").agg((_sum("globalActivePower") / (60*24)).alias("daily_total_power"))
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daily_total_power_pd = daily_total_power.orderBy("date").toPandas()
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plt.figure(figsize=(10, 6))
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plt.plot(daily_total_power_pd['date'], daily_total_power_pd['daily_total_power'], marker='o')
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plt.title('Daily Power Consumption for One Week')
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plt.xlabel('Date')
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plt.ylabel('Total Power Consumption (kW)')
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plt.grid(True)
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plt.xticks(rotation=45)
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plt.tight_layout()
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plt.show()
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spark.stop()
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110
equiepment_averge_power.py
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110
equiepment_averge_power.py
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@ -0,0 +1,110 @@
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import sys
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from pyspark.sql import SparkSession
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from pyspark.sql.functions import col, sum as _sum, hour, avg, expr
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import happybase
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import matplotlib.pyplot as plt
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def convert_date(date_string):
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return f"{date_string[:4]}-{date_string[4:6]}-{date_string[6:]}"
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def convert_time(time_string):
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return f"{time_string[:2]}:{time_string[2:4]}:{time_string[4:]}"
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def get_region_code(dateline):
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year = dateline[:4]
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region_num = (int(year) - 2006) % 6
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return str(region_num)
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def get_double_rowkey(input_date):
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split = "_"
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start_datetime = input_date + "000000"
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stop_datetime = input_date + "235900"
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start_region_code = get_region_code(start_datetime)
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stop_region_code = get_region_code(stop_datetime)
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return start_region_code + split + start_datetime, stop_region_code + split + stop_datetime
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spark = SparkSession.builder \
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.appName("HBaseSparkIntegration") \
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.getOrCreate()
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hbase_host = "100.64.0.3"
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hbase_table = "elec:eleclog"
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connection = happybase.Connection(hbase_host)
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table = connection.table(hbase_table)
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column_family = "info"
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input_date = input("input date (20061217):")
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# input_date = "20061230"
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start_row, stop_row = get_double_rowkey(input_date)
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def fetch_hbase_data(table):
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none_num = 0
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rows = []
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for key, data in table.scan(row_start=start_row.encode("utf-8"), row_stop=stop_row.encode("utf-8")):
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date = data[f'{column_family}:date'.encode('utf-8')].decode('utf-8')
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time = data[f'{column_family}:time'.encode('utf-8')].decode('utf-8')
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value = data[f'{column_family}:globalActivePower'.encode('utf-8')].decode('utf-8')
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if value == "?":
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none_num += 1
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continue
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global_active_power = float(value)
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sub_metering_1 = float(data[f'{column_family}:subMetering1'.encode('utf-8')].decode('utf-8'))
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sub_metering_2 = float(data[f'{column_family}:subMetering2'.encode('utf-8')].decode('utf-8'))
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sub_metering_3 = float(data[f'{column_family}:subMetering3'.encode('utf-8')].decode('utf-8'))
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datetime = f"{convert_date(date)} {convert_time(time)}"
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rows.append((datetime, global_active_power, sub_metering_1, sub_metering_2, sub_metering_3))
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return rows, none_num
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hbase_data, none_num = fetch_hbase_data(table)
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if len(hbase_data) == 0:
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print("No data searched, please confirm your input")
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sys.exit(0)
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if none_num / len(hbase_data) >= 0.20:
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print("This batch data has too many nulls to be of analytical value ")
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sys.exit(0)
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columns = ["datetime", "global_active_power", "sub_metering_1", "sub_metering_2", "sub_metering_3"]
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df = spark.createDataFrame(hbase_data, columns)
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df = df.withColumn("hour", hour(col("datetime")))
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df = df.withColumn("other_consumption", expr("global_active_power * 1000 / 60 - sub_metering_1 - sub_metering_2 - sub_metering_3"))
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hourly_consumption = df.groupBy("hour").agg(
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avg("sub_metering_1").alias("avg_sub_metering_1"),
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avg("sub_metering_2").alias("avg_sub_metering_2"),
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avg("sub_metering_3").alias("avg_sub_metering_3"),
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avg("other_consumption").alias("avg_other_consumption")
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)
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hourly_consumption_pd = hourly_consumption.orderBy("hour").toPandas()
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plt.figure(figsize=(12, 8))
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plt.plot(hourly_consumption_pd['hour'], hourly_consumption_pd['avg_sub_metering_1'], marker='o', label='Sub Metering 1')
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plt.plot(hourly_consumption_pd['hour'], hourly_consumption_pd['avg_sub_metering_2'], marker='o', label='Sub Metering 2')
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plt.plot(hourly_consumption_pd['hour'], hourly_consumption_pd['avg_sub_metering_3'], marker='o', label='Sub Metering 3')
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plt.plot(hourly_consumption_pd['hour'], hourly_consumption_pd['avg_other_consumption'], marker='o', label='Other Consumption')
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plt.title(f'Hourly Consumption for {input_date}')
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plt.xlabel('Hour of the Day')
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plt.ylabel('Total Consumption (Watt-hours)')
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plt.legend()
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plt.grid(True)
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plt.xticks(range(24))
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plt.tight_layout()
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plt.show()
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spark.stop()
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96
hourly_total_power.py
Normal file
96
hourly_total_power.py
Normal file
@ -0,0 +1,96 @@
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import sys
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from pyspark.sql import SparkSession
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from pyspark.sql.functions import col, sum as _sum, hour
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import happybase
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import matplotlib.pyplot as plt
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def convert_date(date_string):
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return f"{date_string[:4]}-{date_string[4:6]}-{date_string[6:]}"
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def convert_time(time_string):
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return f"{time_string[:2]}:{time_string[2:4]}:{time_string[4:]}"
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def get_region_code(dateline):
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year = dateline[:4]
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region_num = (int(year) - 2006) % 6
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return str(region_num)
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def get_double_rowkey(input_date):
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split = "_"
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start_datetime = input_date + "000000"
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stop_datetime = input_date + "235900"
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start_region_code = get_region_code(start_datetime)
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stop_region_code = get_region_code(stop_datetime)
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return start_region_code + split + start_datetime, stop_region_code + split + stop_datetime
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spark = SparkSession.builder \
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.appName("HBaseSparkIntegration") \
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.getOrCreate()
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hbase_host = "100.64.0.3"
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hbase_table = "elec:eleclog"
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connection = happybase.Connection(hbase_host)
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table = connection.table(hbase_table)
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column_family = "info"
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input_date = input("input date (20061217):")
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# input_date = "20061230"
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start_row, stop_row = get_double_rowkey(input_date)
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def fetch_hbase_data(table):
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none_num = 0
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rows = []
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for key, data in table.scan(row_start=start_row.encode("utf-8"), row_stop=stop_row.encode("utf-8")):
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date = data[f'{column_family}:date'.encode('utf-8')].decode('utf-8')
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time = data[f'{column_family}:time'.encode('utf-8')].decode('utf-8')
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value = data[f'{column_family}:globalActivePower'.encode('utf-8')].decode('utf-8')
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if value == "?":
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none_num += 1
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continue
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global_active_power = float(value)
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datetime = f"{convert_date(date)} {convert_time(time)}"
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rows.append((datetime, global_active_power))
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return rows, none_num
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hbase_data, none_num = fetch_hbase_data(table)
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if len(hbase_data) == 0:
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print("No data searched, please confirm your input")
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sys.exit(0)
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if none_num / len(hbase_data) >= 0.20:
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print("This batch data has too many nulls to be of analytical value ")
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sys.exit(0)
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columns = ["datetime", "globalActivePower"]
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df = spark.createDataFrame(hbase_data, columns)
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||||
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df = df.withColumn("hour", hour(col("datetime")))
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hourly_total_power = df.groupBy("hour").agg((_sum("globalActivePower") / 60).alias("hourly_total_power"))
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print(hourly_total_power.head())
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||||
hourly_total_power_pd = hourly_total_power.orderBy("hour").toPandas()
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||||
plt.figure(figsize=(10, 6))
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plt.plot(hourly_total_power_pd['hour'], hourly_total_power_pd['hourly_total_power'], marker='o')
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plt.title(f'Hourly Power Consumption for {input_date}')
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||||
plt.xlabel('Hour of the Day')
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plt.ylabel('Total Power Consumption (kW)')
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plt.grid(True)
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plt.xticks(range(24))
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plt.show()
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||||
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spark.stop()
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113
monthly_total_power.py
Normal file
113
monthly_total_power.py
Normal file
@ -0,0 +1,113 @@
|
||||
import sys
|
||||
from datetime import datetime, timedelta
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql.functions import col, sum as _sum, dayofmonth, date_format
|
||||
import happybase
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
def convert_date(date_string):
|
||||
return f"{date_string[:4]}-{date_string[4:6]}-{date_string[6:]}"
|
||||
|
||||
|
||||
def convert_time(time_string):
|
||||
return f"{time_string[:2]}:{time_string[2:4]}:{time_string[4:]}"
|
||||
|
||||
|
||||
def get_region_code(dateline):
|
||||
year = dateline[:4]
|
||||
region_num = (int(year) - 2006) % 6
|
||||
return str(region_num)
|
||||
|
||||
|
||||
def get_month_dates(input_date):
|
||||
date = datetime.strptime(input_date, "%Y%m")
|
||||
|
||||
start_of_month = date.replace(day=1)
|
||||
|
||||
next_month = start_of_month.replace(month=start_of_month.month % 12 + 1,
|
||||
year=start_of_month.year + (start_of_month.month // 12))
|
||||
end_of_month = next_month - timedelta(days=1)
|
||||
|
||||
start_of_month_str = start_of_month.strftime("%Y%m%d")
|
||||
end_of_month_str = end_of_month.strftime("%Y%m%d")
|
||||
|
||||
return start_of_month_str, end_of_month_str
|
||||
|
||||
|
||||
|
||||
def get_double_rowkey(input_date):
|
||||
split = "_"
|
||||
start_date, stop_date = get_month_dates(input_date)
|
||||
start_datetime = start_date + "000000"
|
||||
stop_datetime = stop_date + "235900"
|
||||
start_region_code = get_region_code(start_datetime)
|
||||
stop_region_code = get_region_code(stop_datetime)
|
||||
return start_region_code + split + start_datetime, stop_region_code + split + stop_datetime
|
||||
|
||||
|
||||
spark = SparkSession.builder \
|
||||
.appName("HBaseSparkIntegration") \
|
||||
.getOrCreate()
|
||||
|
||||
hbase_host = "100.64.0.3"
|
||||
hbase_table = "elec:eleclog"
|
||||
|
||||
connection = happybase.Connection(hbase_host)
|
||||
table = connection.table(hbase_table)
|
||||
|
||||
column_family = "info"
|
||||
|
||||
input_date = input("input date (200701):")
|
||||
# input_date = "20061230"
|
||||
start_row, stop_row = get_double_rowkey(input_date)
|
||||
|
||||
print(start_row, stop_row)
|
||||
|
||||
def fetch_hbase_data(table):
|
||||
none_num = 0
|
||||
rows = []
|
||||
for key, data in table.scan(row_start=start_row.encode("utf-8"), row_stop=stop_row.encode("utf-8")):
|
||||
date = data[f'{column_family}:date'.encode('utf-8')].decode('utf-8')
|
||||
time = data[f'{column_family}:time'.encode('utf-8')].decode('utf-8')
|
||||
value = data[f'{column_family}:globalActivePower'.encode('utf-8')].decode('utf-8')
|
||||
if value == "?":
|
||||
none_num += 1
|
||||
continue
|
||||
global_active_power = float(value)
|
||||
datetime = f"{convert_date(date)} {convert_time(time)}"
|
||||
|
||||
rows.append((datetime, global_active_power))
|
||||
return rows, none_num
|
||||
|
||||
|
||||
hbase_data, none_num = fetch_hbase_data(table)
|
||||
|
||||
if len(hbase_data) == 0:
|
||||
print("No data searched, please confirm your input")
|
||||
sys.exit(0)
|
||||
|
||||
if none_num / len(hbase_data) >= 0.20:
|
||||
print("This batch data has too many nulls to be of analytical value ")
|
||||
sys.exit(0)
|
||||
|
||||
columns = ["datetime", "globalActivePower"]
|
||||
df = spark.createDataFrame(hbase_data, columns)
|
||||
|
||||
df = df.withColumn("date", date_format(col("datetime"), "yyyy-MM-dd"))
|
||||
|
||||
daily_total_power = df.groupBy("date").agg((_sum("globalActivePower") / (60*24)).alias("daily_total_power"))
|
||||
|
||||
daily_total_power_pd = daily_total_power.orderBy("date").toPandas()
|
||||
|
||||
plt.figure(figsize=(10, 6))
|
||||
plt.plot(daily_total_power_pd['date'], daily_total_power_pd['daily_total_power'], marker='o')
|
||||
plt.title('Daily Power Consumption for One Mouth')
|
||||
plt.xlabel('Date')
|
||||
plt.ylabel('Total Power Consumption (kW)')
|
||||
plt.grid(True)
|
||||
plt.xticks(rotation=45)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
spark.stop()
|
||||
Loading…
Reference in New Issue
Block a user