Dev0

environment.yml

@@ -5,7 +5,7 @@ dependencies:
   - python=>3.9
   - xarray
   - matplotlib>=3.1
-  - numpy>=1.17
+  - numpy>=2.0
   - pandas>=2.2.1
   - pip
   - scipy

examples/example_report.py → examples/example_report_AI.py

@@ -5,6 +5,10 @@
 from docx.shared import Inches
 import warnings
 warnings.filterwarnings("ignore")
+import google.generativeai as genai
+import os
+genai.configure(api_key=os.environ["GEMINI_API_KEY"])
+model = genai.GenerativeModel(model_name="gemini-1.5-flash")
 
 df = readNora10File('../tests/data/NORA_test.txt') 
 
@@ -49,10 +53,13 @@
 
 # Add the first table
 df = tables.table_monthly_non_exceedance(ds,var= 'W10',step_var=2,output_file=None)
-doc.add_heading('Table 1: Monthly non-exceedance', level=2)
+response = model.generate_content("Write one sentence caption to the table:"+str(df))
+#doc.add_heading('Table 1: Monthly non-exceedance', level=2)
+doc.add_heading('Table 1:'+response.text, level=2)
 table1 = doc.add_table(rows=df.shape[0] + 1, cols=df.shape[1])
 table1.style = 'Table Grid'
 
+
 # Add the header row for the first table
 hdr_cells = table1.rows[0].cells
 for i, column in enumerate(df.columns):

metocean_stats/stats/aux_funcs.py

@@ -124,13 +124,17 @@ def Hs_Tp_curve(data,pdf_Hs,pdf_Hs_Tp,f_Hs_Tp,h,t,interval,X=100):
 
     # Find index of Hs=value
     epsilon = abs(h - rve_X)
-    param = find_peaks(1/epsilon) # to find the index of bottom
-    index = param[0][0]     # the  index of Hs=value
+    param, prov = find_peaks(1/epsilon) # to find the index of bottom
+    if len(param) == 0:
+        param = [np.argmax(1/epsilon)] 
+    index = param[0]     # the  index of Hs=value
 
     # Find peak of pdf at Hs=RVE of X year 
     pdf_Hs_Tp_X = pdf_Hs_Tp[index,:] # Find pdf at RVE of X year 
-    param = find_peaks(pdf_Hs_Tp_X) # find the peak
-    index = param[0][0]
+    param, prov = find_peaks(pdf_Hs_Tp_X) # find the peak
+    if len(param) == 0:
+        param = [np.argmax(pdf_Hs_Tp_X)] 
+    index = param[0]
     f_Hs_Tp_100=pdf_Hs_Tp_X[index]
 
 

tests/test_plots.py

@@ -31,15 +31,6 @@ def test_plot_prob_non_exceedance_fitted_3p_weibull(ds=ds):
     else:
         raise ValueError("FigValue is not correct")
 
-#def test_scatter_diagram(ds=ds):
-#    output_file = 'test_scatter_diagram.csv'
-#    df = tables.scatter_diagram(ds, var1='HS', step_var1=1, var2='TP', step_var2=1, output_file=output_file)
-#    if os.path.exists(output_file):
-#        os.remove(output_file)
-#    if df.shape[0] == 14:
-#        pass
-#    else:
-#        raise ValueError("Shape is not correct")
 
 def test_plot_monthly_stats(ds=ds):
     output_file = 'test_monthly_stats.png'

tests/test_tables.py

@@ -10,15 +10,13 @@
 depth = ['0m', '1m', '2.5m', '5m', '10m', '15m', '20m', '25m', '30m', '40m', '50m', '75m', '100m', '150m', '200m']
 
 
-#def test_scatter_diagram(ds=ds):
-#    output_file = 'test_scatter_diagram.csv'
-#    df = tables.scatter_diagram(ds, var1='HS', step_var1=1, var2='TP', step_var2=1, output_file=output_file)
-#    if os.path.exists(output_file):
-#        os.remove(output_file)
-#    if df.shape[0] == 14:
-#        pass
-#    else:
-#        raise ValueError("Shape is not correct")
+def test_scatter_diagram(ds=ds):
+    output_file = 'test_scatter_diagram.csv'
+    df = tables.scatter_diagram(ds, var1='HS', step_var1=1, var2='TP', step_var2=1, output_file=output_file)
+    if os.path.exists(output_file):
+        os.remove(output_file)
+    assert df.shape[0] == 14
+
 
 def test_table_var_sorted_by_hs(ds=ds):
     output_file = 'test_var_sorted_by_hs.csv'