Improved bokeh plots

crisien · May 9, 2020 · 0dd937c · 0dd937c
1 parent 2fc7e0f
commit 0dd937c
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Showing 6 changed files with 45 additions and 30 deletions.
diff --git a/python/Arkansas_hybrid_pcp_index.html b/python/Arkansas_hybrid_pcp_index.html
diff --git a/python/addtional_examples/Iowa_hybrid_pcp_index.html b/python/addtional_examples/Iowa_hybrid_pcp_index.html
diff --git a/python/addtional_examples/Washington_hybrid_pcp_index.html b/python/addtional_examples/Washington_hybrid_pcp_index.html
diff --git a/python/addtional_examples/hybrid_index_example_script_iowa.py b/python/addtional_examples/hybrid_index_example_script_iowa.py
@@ -7,11 +7,16 @@
 import pandas as pd
 from bokeh.plotting import figure, output_file, show
 import hybrid_pcp_index
+import numpy as np
 
 #load Iowa precipiation anomalies and PDSI time series
 df = pd.read_csv (r'pcp_pdsi_iowa.csv')
 pcp00 = df.values[:,2];
 pdsi = df.values[:,3];
+#rename year col. and convert to datetime
+df = df.rename(columns={'%year':'year'})
+df.insert(2,"day",list(np.ones(833).astype(int)),True)
+datetime = pd.to_datetime(df[['year','month','day']])
 
 #load f^-1 spectrum
 evapo = pd.read_csv (r'evapo.csv')
@@ -38,9 +43,9 @@
 
 #Plot the results for Iowa
 output_file("Iowa_hybrid_pcp_index.html", title="Iowa")
-plot = figure(title= "Iowa", x_axis_label= 'time', y_axis_label= 'index')
-plot.line(list(range(833)), pcpexp3, legend_label="Tau03", line_color="black", line_width = 2)
-plot.line(list(range(833)), pcpexp10, legend_label="Tau10", line_color="blue", line_width = 2)
-plot.line(list(range(833)), pcpexp20, legend_label="Tau20", line_color="green", line_width = 2)
-plot.line(list(range(833)), pcpexp36, legend_label="Tau36", line_color="red", line_width = 2)
+plot = figure(title= "Iowa", x_axis_type='datetime', x_axis_label= 'time', y_axis_label= 'index')
+plot.line(datetime, pcpexp3, legend_label="Tau03", line_color="black", line_width = 2)
+plot.line(datetime, pcpexp10, legend_label="Tau10", line_color="blue", line_width = 2)
+plot.line(datetime, pcpexp20, legend_label="Tau20", line_color="green", line_width = 2)
+plot.line(datetime, pcpexp36, legend_label="Tau36", line_color="red", line_width = 2)
 show(plot)
diff --git a/python/addtional_examples/hybrid_index_example_script_washington.py b/python/addtional_examples/hybrid_index_example_script_washington.py
@@ -7,11 +7,16 @@
 import pandas as pd
 from bokeh.plotting import figure, output_file, show
 import hybrid_pcp_index
+import numpy as np
 
 #load Washington precipiation anomalies and PDSI time series
 df = pd.read_csv (r'pcp_pdsi_washington.csv')
 pcp00 = df.values[:,2];
 pdsi = df.values[:,3];
+#rename year col. and convert to datetime
+df = df.rename(columns={'%year':'year'})
+df.insert(2,"day",list(np.ones(833).astype(int)),True)
+datetime = pd.to_datetime(df[['year','month','day']])
 
 #load f^-1 spectrum
 evapo = pd.read_csv (r'evapo.csv')
@@ -38,9 +43,9 @@
 
 #Plot the results for Washington
 output_file("Washington_hybrid_pcp_index.html", title="Washington")
-plot = figure(title= "Washington", x_axis_label= 'time', y_axis_label= 'index')
-plot.line(list(range(833)), pcpexp3, legend_label="Tau03", line_color="black", line_width = 2)
-plot.line(list(range(833)), pcpexp10, legend_label="Tau10", line_color="blue", line_width = 2)
-plot.line(list(range(833)), pcpexp20, legend_label="Tau20", line_color="green", line_width = 2)
-plot.line(list(range(833)), pcpexp36, legend_label="Tau36", line_color="red", line_width = 2)
+plot = figure(title= "Washington", x_axis_type='datetime', x_axis_label= 'time', y_axis_label= 'index')
+plot.line(datetime, pcpexp3, legend_label="Tau03", line_color="black", line_width = 2)
+plot.line(datetime, pcpexp10, legend_label="Tau10", line_color="blue", line_width = 2)
+plot.line(datetime, pcpexp20, legend_label="Tau20", line_color="green", line_width = 2)
+plot.line(datetime, pcpexp36, legend_label="Tau36", line_color="red", line_width = 2)
 show(plot)
diff --git a/python/hybrid_index_example_script_arkansas.py b/python/hybrid_index_example_script_arkansas.py
@@ -7,11 +7,16 @@
 import pandas as pd
 from bokeh.plotting import figure, output_file, show
 import hybrid_pcp_index
+import numpy as np
 
 #load Arkansas precipiation anomalies and PDSI time series
 df = pd.read_csv (r'pcp_pdsi_arkansas.csv')
 pcp00 = df.values[:,2];
 pdsi = df.values[:,3];
+#rename year col. and convert to datetime
+df = df.rename(columns={'%year':'year'})
+df.insert(2,"day",list(np.ones(833).astype(int)),True)
+datetime = pd.to_datetime(df[['year','month','day']])
 
 #load f^-1 spectrum
 evapo = pd.read_csv (r'evapo.csv')
@@ -38,9 +43,9 @@
 
 #Plot the results for Arkansas
 output_file("Arkansas_hybrid_pcp_index.html", title="Arkansas")
-plot = figure(title= "Arkansas", x_axis_label= 'time', y_axis_label= 'index')
-plot.line(list(range(833)), pcpexp3, legend_label="Tau03", line_color="black", line_width = 2)
-plot.line(list(range(833)), pcpexp10, legend_label="Tau10", line_color="blue", line_width = 2)
-plot.line(list(range(833)), pcpexp20, legend_label="Tau20", line_color="green", line_width = 2)
-plot.line(list(range(833)), pcpexp36, legend_label="Tau36", line_color="red", line_width = 2)
+plot = figure(title= "Arkansas", x_axis_type='datetime', x_axis_label= 'time', y_axis_label= 'index')
+plot.line(datetime, pcpexp3, legend_label="Tau03", line_color="black", line_width = 2)
+plot.line(datetime, pcpexp10, legend_label="Tau10", line_color="blue", line_width = 2)
+plot.line(datetime, pcpexp20, legend_label="Tau20", line_color="green", line_width = 2)
+plot.line(datetime, pcpexp36, legend_label="Tau36", line_color="red", line_width = 2)
 show(plot)