Data visualization

Setelah membaca ini, pembaca diharapkan mampu memvisualisasikan data meteorologi maritim dalam bentuk grafik dan plot sederhana.

Load Data

import pandas as pd
import matplotlib.pyplot as plt
import datetime
import numpy as np

df = pd.read_cv('../data/aws_priok_202109.csv')
df['time'] = pd.to_datetime(df['time'])
df = df.resample('1D', on='time').mean()
df.head()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[1], line 6
      3 import datetime
      4 import numpy as np
----> 6 df = pd.read_cv('../data/aws_priok_202109.csv')
      7 df['time'] = pd.to_datetime(df['time'])
      8 df = df.resample('1D', on='time').mean()

AttributeError: module 'pandas' has no attribute 'read_cv'

# Ambil parameter temp dan watertemp
df = df[['rh','temp','watertemp']]
df

	rh	temp	watertemp
time
2021-09-01	72.573371	29.460338	30.752212
2021-09-02	70.542498	29.064415	30.808327
2021-09-03	70.765353	28.904549	30.836240
2021-09-04	69.039739	29.169870	31.168078
2021-09-05	73.956325	29.269753	31.169611
2021-09-06	73.130916	29.126870	31.075267
2021-09-07	80.551849	27.442912	30.733359
2021-09-08	79.045159	27.560707	30.899647
2021-09-09	65.145878	28.714809	31.008702
2021-09-10	65.530705	29.318726	31.098408
2021-09-11	71.481535	29.371063	30.972502
2021-09-12	75.613602	28.671599	30.698580
2021-09-13	76.104721	28.851062	30.327695
2021-09-14	85.098460	26.080956	29.531442
2021-09-15	81.604671	28.254211	29.948832
2021-09-16	77.980704	28.956273	30.545194
2021-09-17	72.613714	29.531505	30.697183
2021-09-18	65.422942	30.657356	30.856830
2021-09-19	83.249879	26.794903	31.142961
2021-09-20	72.800731	29.093348	30.881287
2021-09-21	71.432129	28.920660	31.035428
2021-09-22	67.668648	29.701982	31.107077
2021-09-23	66.292313	29.866471	30.829209
2021-09-24	73.204901	29.416167	30.746891
2021-09-25	75.983845	29.463596	30.942690
2021-09-26	81.547313	28.254031	31.062023
2021-09-27	81.034595	28.951628	31.186374
2021-09-28	75.958053	29.176940	31.569693
2021-09-29	74.932884	29.491690	31.825426
2021-09-30	76.530015	28.707980	31.900073

Line & Bar Plot

# LINE PLOT

plt.figure(figsize=(10,5), dpi=300)
plt.plot(df.index, df['watertemp'], color='b', marker='o', label='watertemp')
plt.plot(df.index, df['temp'], color='r', marker='d', label='airtemp')
plt.xtiks(rotation=40)
plt.xlabel("Time")
plt.ylabel("Temp (degC)")
plt.title("Plot Watertemp dan Airtemp di AWS TJ Priok")
plt.legend(loc='lower right')

<matplotlib.legend.Legend at 0x7f2538c67ed0>

# BAR PLOT

width = 0.2
plt.figure(figsize=(10,5), dpi=300)
plt.bar(df.index-datetime.timedelta(hours=5), df['watertemp'], color='b', label='watertemp', align='edge', width=0.4)
plt.bar(df.index+datetime.timedelta(hours=5), df['temp'], color='r', label='airtemp', align='edge', width=0.4)
plt.xticks(rotation=40)
plt.xlabel("Time")
plt.ylabel("Temp (degC)")
plt.title("Plot Watertemp dan Airtemp di AWS TJ Priok")
plt.legend(loc='lower right', bbox_to_anchor=(1.2,0.05))
fig.tight_layout()
plt.show()

# LINE PLOT 2 AXES

fig, ax1 = plt.subplots(figsize=(10,5))
line1, = ax1.plot(df.index, df['temp'], color='b', marker='o', label='airtemp')
ax1.set_xlabel("Time")
ax1.set_ylabel("Temp (degC)", color='b')
ax1.tick_params(axis='y', labelcolor='b')
plt.legend(loc=lower right')

ax2 = ax1.twinx()
line2, = ax2.plot(df.index, df['rh'], color='r', marker='d', label='relhum')
ax2.set_ylabel("RH (%)", color='r')
ax2.tick_params(axis='y', labelcolor='r')

plt.title("Plot Airtemp dan RH di AWS TJ Priok")
lines = [line1, line2]
labels = [line.get_label() for line in lines]
ax1.legend([line1, line2], labels, loc='lower right')

fig.tight_layout()
ax1.set_xticks(ticks=df.index[::2], labels= df.index[::2], rotation=60)
plt.show()

Scatter Plot

fig, ax = plt.subplots(figsize=(10,5))
ax.scatter(df['temp'], df['rh'])
ax.set_xlabel("Temp (degC)")
ax.set_ylabel("RH (%)")
plt.title("Scatter Plot Airtemp dan RH di AWS TJ Priok")
plt.legend(loc='lower right')

z = np.polyfit(df['temp'], df['rh'], 1)
p = np.poly1d(z
ax.plot(df['temp'],p(df['temp']),"r--")

plt.show()

/tmp/ipykernel_31756/3468112991.py:7: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend(loc='lower right')

import pooch
import numpy as np
import matplotlib.pyplot as plt

fname = pooch.retrieve(
    "https://rabernat.github.io/research_computing/signif.txt.tsv.zip",
    known_hash='22b9f7045bf90fb99e14b95b24c81da3c52a0b4c79acf95d72fbe3a257001dbb',
    processor=pooch.Unzip()
)[0]

earthquakes = np.genfromtxt(fname, delimiter='\t')
depth = earthquakes[:, 8]
magnitude = earthquakes[:, 9]
latitude = earthquakes[:, 20]
longitude = earthquakes[:, 21]

plt.scatter(longitude, latitude, c=magnitude, cmap='viridis')
plt.colorbar()

<matplotlib.colorbar.Colorbar at 0x7fa4c68c3230>

Histogram

fig, ax = plt.subplots(figsize=(8,5))
counts, bins, patches = ax.hist(df['temp'], density=True, range=(26,32), color='lightgreen', edgecolor='black', bins=6)

for count, bin_start n zip(counts, bins):
    bin_center = bin_start + (bins[1] - bins[0])/2
    ax.text(bin_center, count, f"{count:.3f}", horizontalalignment='center', verticalalignment='bottom')

ax.set_xlabel("Temp (degC)")
ax.set_ylabel("Density")
plt.title("Histogram Plot Airtemp di AWS TJ Priok")

plt.show()

Multiple Plot

fig, ax = plt.subplots(figsize=(20, 10), ncols=2, nrows=2)

# =======================================================================================
# Top-left: Time series with dual y-axis
line1, = ax[0,0].pot(df.index, df['temp'], color='b', marker='o', label='airtemp')
ax[0,0].set_xlabel("Time")
ax[0,0].set_ylabel("Temp (degC)", color='b')
ax[0,0].tick_params(axis='y', labelcolor='b')
ax[0,0].set_title("Plot Airtemp dan RH di AWS TJ Priok")
ax[0,0].set_xticks(df.index[::2])
ax[0,0].set_xticklabels(df.index[::2], rotation=60)

ax00copy = ax[0,0].twinx()
line2, = ax00copy.plot(df.index, df['rh'], color='r', marker='d', label='relhum')
ax00copy.set_ylabel("RH (%)", color='r')
ax00copy.tick_params(axis='y', labelcolor='r')

lines = [line1, line2]
labels = [line.get_label() for line in lines]
ax[0,0].legend(lines, labels, loc='lower right')

# =======================================================================================
# Top-right: Grouped bar plot
width = 0.2
ax[0,1].bar(df.index - datetime.timedelta(hours=5), df['watertemp'], color='b', label='watertemp', align='edge', width=0.4)
ax[0,1].bar(df.index + datetime.timedelta(hours=5), df['temp'], color='r', label='airtemp', align='edge', width=0.4)
ax[0,1].set_xticks(f.index[::2])
ax[0,1].set_xticklabels(df.index[::2], rotation=60)
ax[0,1].set_xlabel("Time")
ax[0,1].set_ylabel("Temp (degC)")
ax[0,1].set_title("Plot Watertemp dan Airtemp di AWS TJ Priok")
ax[0,1].legend(loc='upper left')

# =======================================================================================
# Bottom-left: Scatter plot with trendline
ax[1,0].scatter(df['temp'], df['rh'], label='Data')
ax[1,0].set_xlabel("Temp (degC)")
ax[1,0].set_ylabel("RH (%)")
ax[1,0].set_title("Scatter Plot Airtemp dan RH di AWS TJ Priok")

z = np.polyfit(df['temp'], df['rh'], 1)
p = np.poly1d(z)
ax[1,0].plot(df['temp'], p(df['temp']), "r--", label='Trendline')
ax[1,0].legend(loc='lower right')

# =======================================================================================
# Bottom-right: Histogram
counts, bins, patches = ax[1,1].hist(df['temp'], density=True, range=(26, 32), color='lightgreen', edgecolor='black', bins=6)

for count, bin_start in zip(counts, bins[:-1]):  # bins[:-1] because len(bins) = len(counts)+1
    bin_center = bin_start + (bins[1] - bins[0])/2
    ax[1,1].text(bin_center, count, f"{count:.3f}", ha='center', va='bottom')

ax[1,1].set_xlabel("Temp (degC)")
ax[1,1].set_ylabel("Density")
ax[1,1].settitle("Histogram Plot Airtemp di AWS TJ Priok")

# =======================================================================================
fig.suptitle("Visualisasi Data AWS TJ Priok", fontsize=20, fontweight='bold')
plt.tight_layout(rect=[0, 0, 1, 0.96])
# plt.tight_layout()
plt.show()