Fix issues with latest python version

Issue 1: Panda has depricated .append() function. Fix this by using Dataframe's loc function Issue 2: Label object in bokeh has depricated `render_mode` attribute, so remove that in object initilization Issue 3: plot_width in figure objest is depricated, use width instead. Issue 4: Dataframe.drop() has also attribute changes, fix this by only passing colomn name. Signed-off-by: Wyes Karny <[email protected]>
ednunezg · Oct 15, 2023 · 61d2ced · 61d2ced
1 parent 64d5566
commit 61d2ced
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Showing 2 changed files with 28 additions and 29 deletions.
diff --git a/pytrendline/detect.py b/pytrendline/detect.py
@@ -344,32 +344,31 @@ def detect_wrapped(tt):
 
         score = config.get("scoring_function", DEFAULT_CONFIG["scoring_function"])(candlestick_data, err_distances, num_points, slope)
 
-        trends_df = trends_df.append({
-          'id':              pointset_id,
-          'trendtype':       tt,
-          'pointset_indeces':points_in_trendline,
-          'pointset_dates':  [candlestick_data.df.iloc[pt].Date for pt in points_in_trendline],
-          'starts_at_index': points_in_trendline[0],
-          'starts_at_date':  candlestick_data.df.iloc[points_in_trendline[0]].Date,
-          'ends_at_index':   points_in_trendline[-1],
-          'ends_at_date':    candlestick_data.df.iloc[points_in_trendline[-1]].Date,
-          'is_breakout':     is_breakout,
-          'breakout_index':  breakout_index,
-          'breakout_date':   breakout_date,
-          'num_points':      num_points,
-          'm': m,
-          'b': b,
-          'slope': slope,
-          'price_at_last_date': trend_price_at_last,
-          'score': score,
-          'global_maxs_or_mins': global_max_or_mins,
-          'includes_global_max_or_min': global_pt_found,
-          'price_at_next_future_date': trend_price_at_last + m,
-          'duplicate_group_id': None,
-          'is_best_from_duplicate_group': False,
-          'overall_rank': None,
-          'rank_within_group': 0
-        }, ignore_index=True)
+        trends_df.loc[len(trends_df.index)] = [
+            pointset_id,
+            tt,
+            points_in_trendline,
+            [candlestick_data.df.iloc[pt].Date for pt in points_in_trendline],
+            points_in_trendline[0],
+            candlestick_data.df.iloc[points_in_trendline[0]].Date,
+            points_in_trendline[-1],
+            candlestick_data.df.iloc[points_in_trendline[-1]].Date,
+            is_breakout,
+            breakout_index,
+            breakout_date,
+            num_points,
+            m,
+            b,
+            slope,
+            trend_price_at_last,
+            score,
+            global_pt_found,
+            global_max_or_mins,
+            trend_price_at_last + m,
+            None,
+            False,
+            None,
+            0]
 
     # Mark which of the trendlines are duplicate
     trends_df = _mark_duplicates(trends_df, candlestick_data, tt, config)

diff --git a/pytrendline/plot.py b/pytrendline/plot.py
@@ -131,7 +131,7 @@ def plot_figure(self, p, candles_df, opts={}):
       label_y_pos = tl_y_at_last_date + (2 * m)
 
       label = Label(x=label_x_pos, y=label_y_pos,
-                 text=label_text, render_mode='css',
+                 text=label_text,
                  border_line_color=color, border_line_alpha=0.8,
                  background_fill_color='white', background_fill_alpha=1.0)
       p.segment(
@@ -255,7 +255,7 @@ def plot_graph_bokeh(results):
   p = figure(
     x_axis_type="datetime",
     tools="pan,wheel_zoom,tap,crosshair,hover,poly_draw,reset,save",
-    plot_width=1300,
+    width=1300,
     title=site_title,
     y_range=(y_range_top, y_range_bottom),
     x_range=(x_range_left, x_range_right),
@@ -308,7 +308,7 @@ def plot_table_bokeh(results):
     all_results = results['resistance_trendlines']
 
   if len(all_results) > 0:
-    html_trends_table = all_results.drop('pointset_dates', 1).to_html(border=0, header=True, index=False, justify="left", float_format=lambda x: '%10.3f' % x)
+    html_trends_table = all_results.drop(columns='pointset_dates').to_html(border=0, header=True, index=False, justify="left", float_format=lambda x: '%10.3f' % x)
   else:
     html_trends_table = '<p>No trendlines found</p>'