correct homework

Author: alvinhuu

D23 BOKEH/homework.py

@@ -8,11 +8,127 @@
 # 讓網頁直接輸出在NOTEBOOK
 output_notebook()
 
+#Bokeh官方有提供sample_data給大家練習，gallery豐富的範例都取自sample_data。
+#下載sample_data指令為bokeh.sampledata.download()，直接貼在jupyter執行。檔案會下載到bokeh module裡。
+import bokeh.sampledata
+bokeh.sampledata.download()
+
+
 # 基本條形圖 條形圖是常見且重要的繪圖類型。通過 Bokeh，可以輕鬆創建各種堆疊或嵌套的條形圖，並通常處理分類數據。 請使用 vbar 方法創建的，用於繪製垂直條。
 # 1.  建立簡單的水果資料集
 # fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
 # counts = [5, 3, 4, 2, 4, 6]
+from bokeh.models import ColumnDataSource
+from bokeh.palettes import Spectral6
+from bokeh.models import ColumnDataSource, HoverTool
+from bokeh.plotting import figure
+
+fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
+counts = [5, 3, 4, 2, 4, 6]
+
+source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6))
+
+p = figure(x_range=fruits, plot_height=250, y_range=(0, 9), title="Fruit Counts")
+p.vbar(x='fruits', top='counts', width=0.9, color='color', legend_field="fruits", source=source)
+
+p.xgrid.grid_line_color = None
+p.legend.orientation = "horizontal"
+p.legend.location = "top_center"
+
+show(p)
 # 2.   利用 Source 建立字典，再用 figure 輸出 BAR 圖
 # source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6))
+from bokeh.models import FactorRange
+from bokeh.transform import factor_cmap
+
+fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
+years = ['2015', '2016', '2017']
+
+data = {'fruits' : fruits,
+        '2015'   : [2, 1, 4, 3, 2, 4],
+        '2016'   : [5, 3, 3, 2, 4, 6],
+        '2017'   : [3, 2, 4, 4, 5, 3]}
+
+# this creates [ ("Apples", "2015"), ("Apples", "2016"), ("Apples", "2017"), ("Pears", "2015), ... ]
+x = [ (fruit, year) for fruit in fruits for year in years ]
+counts = sum(zip(data['2015'], data['2016'], data['2017']), ()) # like an hstack
+
+source = ColumnDataSource(data=dict(x=x, counts=counts))
+
+p = figure(x_range=FactorRange(*x), plot_height=250, title="Fruit Counts by Year")
+
+#p.vbar(x='x', top='counts', width=0.9, source=source)
+
+p.vbar(x='x', top='counts', width=0.9, source=source, line_color="white",
+
+       # use the palette to colormap based on the the x[1:2] values
+       fill_color=factor_cmap('x', palette=['firebrick', 'olive', 'navy'], factors=years, start=1, end=2))
+
+p.y_range.start = 0
+p.x_range.range_padding = 0.1
+p.xaxis.major_label_orientation = 1
+p.xgrid.grid_line_color = None
+
+show(p)
 # 3.   Bokeh 官方有提供 sample_data 給大家練習，gallery 豐富的範例都取自 sample_data，對比官方的資料格式就能輕鬆模仿應用，下載 股市資料
-# 4.   使用 HoverTool(游標滑過時顯示資料)；Click_policy (藉由標籤控制數值顯示)
+
+# 4.   使用 HoverTool(游標滑過時顯示資料)；Click_policy (藉由標籤控制數值顯示)
+import bokeh.io
+from bokeh.resources import INLINE
+from bokeh.models import HoverTool
+from bokeh.palettes import Spectral4
+from bokeh.plotting import figure, output_file, show, output_notebook, ColumnDataSource
+from bokeh.sampledata.stocks import AAPL, GOOG, IBM, MSFT
+import pandas as pd
+
+# 環境 settings
+bokeh.io.reset_output()
+bokeh.io.output_notebook(INLINE)
+
+
+# set hover
+## HoverTool
+# 游標滑過時顯示資料,date格式需要轉換，不然會是timestamp
+hover = HoverTool(
+    tooltips = [
+        ("date", "@date"),
+        ("close", "@open"),
+        ("close", "@close"),
+        ("high", "@high"),
+        ("low", "@low"),
+        ("volume","@volume")
+    ], 
+    formatters={"@date":"datetime"}
+)
+
+# set figure
+p = figure(
+    plot_width=1000, 
+    plot_height=400, 
+    x_axis_type="datetime",
+    tools=[hover,"pan,box_zoom,reset,save"],
+)
+p.title.text = 'Stock_Price--Click on legend entries to mute the corresponding lines and show daily details in hover'
+
+# use ColumnDataSource to control
+# click_policy
+# 藉由標籤控制數值顯示
+# hide為隱藏，mute為切換自訂顯示模式
+# 可在muted_color控制顏色, muted_alpha控制顏色濃淡
+
+for data, name, color in zip([AAPL, IBM, MSFT, GOOG], ["AAPL", "IBM", "MSFT", "GOOG"], Spectral4):
+    df = pd.DataFrame(data)
+    df['date'] = pd.to_datetime(df['date'])
+    source = ColumnDataSource(df)
+    p.line(x="date",y="close", line_width=2, color=color, alpha=0.8,
+           muted_color=color, muted_alpha=0.2, legend_label=name,source=source)
+
+
+p.legend.location = "top_left"
+# use hide or mute
+p.legend.click_policy="mute"
+
+# output_file("interactive_legend.html", title="interactive_legend.py example")
+
+show(p)
+output_notebook()