Fixes stocks integration tests (#2862)

* fixed integrated test test_stocks_ba.openbb

* fixed integrated test test_stocks_dd.openbb

* improved and centralised the check

* fix lint

* reverted the generalisation and used it localy instead

* fixed typo and removed comments

* replaced 2 if for or condition

Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com>

openbb_terminal/common/behavioural_analysis/twitter_model.py

@@ -126,7 +126,7 @@ def get_sentiment(
     symbol: str,
     n_tweets: int = 15,
     n_days_past: int = 2,
-):
+) -> pd.DataFrame:
     """Get sentiments from symbol
 
     Parameters
@@ -172,7 +172,9 @@ def get_sentiment(
             end_date=dt_recent.strftime(dt_format),
         )
 
-        if temp.empty:
+        if (isinstance(temp, pd.DataFrame) and temp.empty) or (
+            not isinstance(temp, pd.DataFrame) and not temp
+        ):
             return pd.DataFrame()
 
         df_tweets = pd.concat([df_tweets, temp])

openbb_terminal/common/behavioural_analysis/twitter_view.py

@@ -40,7 +40,9 @@ def display_inference(symbol: str, limit: int = 100, export: str = ""):
     """
     df_tweets = twitter_model.load_analyze_tweets(symbol, limit)
 
-    if df_tweets.empty:
+    if (isinstance(df_tweets, pd.DataFrame) and df_tweets.empty) or (
+        not isinstance(df_tweets, pd.DataFrame) and not df_tweets
+    ):
         return
 
     # Parse tweets
@@ -102,84 +104,85 @@ def display_sentiment(
 
     df_tweets = twitter_model.get_sentiment(symbol, n_tweets, n_days_past)
 
-    if not df_tweets.empty:
+    if df_tweets.empty:
+        return
 
-        ax1, ax2, ax3 = None, None, None
+    ax1, ax2, ax3 = None, None, None
 
-        if compare:
-            # This plot has 3 axes
-            if external_axes is None:
-                _, axes = plt.subplots(
-                    3, 1, sharex=False, figsize=plot_autoscale(), dpi=cfg_plot.PLOT_DPI
-                )
-                ax1, ax2, ax3 = axes
-            elif is_valid_axes_count(external_axes, 3):
-                (ax1, ax2, ax3) = external_axes
-            else:
-                return
+    if compare:
+        # This plot has 3 axes
+        if external_axes is None:
+            _, axes = plt.subplots(
+                3, 1, sharex=False, figsize=plot_autoscale(), dpi=cfg_plot.PLOT_DPI
+            )
+            ax1, ax2, ax3 = axes
+        elif is_valid_axes_count(external_axes, 3):
+            (ax1, ax2, ax3) = external_axes
         else:
-            # This plot has 2 axes
-            if external_axes is None:
-                _, axes = plt.subplots(
-                    2, 1, sharex=True, figsize=plot_autoscale(), dpi=cfg_plot.PLOT_DPI
-                )
-                ax1, ax2 = axes
-            elif is_valid_axes_count(external_axes, 2):
-                (ax1, ax2) = external_axes
-            else:
-                return
+            return
+    else:
+        # This plot has 2 axes
+        if external_axes is None:
+            _, axes = plt.subplots(
+                2, 1, sharex=True, figsize=plot_autoscale(), dpi=cfg_plot.PLOT_DPI
+            )
+            ax1, ax2 = axes
+        elif is_valid_axes_count(external_axes, 2):
+            (ax1, ax2) = external_axes
+        else:
+            return
 
+    ax1.plot(
+        pd.to_datetime(df_tweets["created_at"]),
+        df_tweets["cumulative_compound"].values,
+    )
+    ax1.set_ylabel("\nCumulative\nVADER Sentiment")
+    for _, day_df in df_tweets.groupby(by="Day"):
+        day_df["time"] = pd.to_datetime(day_df["created_at"])
+        day_df = day_df.sort_values(by="time")
         ax1.plot(
-            pd.to_datetime(df_tweets["created_at"]),
-            df_tweets["cumulative_compound"].values,
+            day_df["time"],
+            day_df["sentiment"].cumsum(),
+            label=pd.to_datetime(day_df["date"]).iloc[0].strftime("%Y-%m-%d"),
         )
-        ax1.set_ylabel("\nCumulative\nVADER Sentiment")
-        for _, day_df in df_tweets.groupby(by="Day"):
-            day_df["time"] = pd.to_datetime(day_df["created_at"])
-            day_df = day_df.sort_values(by="time")
-            ax1.plot(
-                day_df["time"],
-                day_df["sentiment"].cumsum(),
-                label=pd.to_datetime(day_df["date"]).iloc[0].strftime("%Y-%m-%d"),
-            )
-            ax2.bar(
-                df_tweets["date"],
-                df_tweets["positive"],
-                color=theme.up_color,
-                width=theme.volume_bar_width / 100,
-            )
         ax2.bar(
             df_tweets["date"],
-            -1 * df_tweets["negative"],
-            color=theme.down_color,
+            df_tweets["positive"],
+            color=theme.up_color,
             width=theme.volume_bar_width / 100,
         )
-        ax1.set_title(
-            f"Twitter's {symbol} total compound sentiment over time is {round(np.sum(df_tweets['sentiment']), 2)}"
-        )
+    ax2.bar(
+        df_tweets["date"],
+        -1 * df_tweets["negative"],
+        color=theme.down_color,
+        width=theme.volume_bar_width / 100,
+    )
+    ax1.set_title(
+        f"Twitter's {symbol} total compound sentiment over time is {round(np.sum(df_tweets['sentiment']), 2)}"
+    )
 
-        theme.style_primary_axis(ax1)
+    theme.style_primary_axis(ax1)
 
-        ax2.set_ylabel("VADER Polarity Scores")
-        theme.style_primary_axis(ax2)
+    ax2.set_ylabel("VADER Polarity Scores")
+    theme.style_primary_axis(ax2)
 
-        if compare:
-            # get stock end price for each corresponding day if compare == True
-            closing_price_df = get_closing_price(symbol, n_days_past)
-            if ax3:
-                ax3.plot(
-                    closing_price_df["Date"],
-                    closing_price_df["Close"],
-                    label=pd.to_datetime(closing_price_df["Date"])
-                    .iloc[0]
-                    .strftime("%Y-%m-%d"),
-                )
+    if compare:
+        # get stock end price for each corresponding day if compare == True
+        closing_price_df = get_closing_price(symbol, n_days_past)
+        if ax3:
+            ax3.plot(
+                closing_price_df["Date"],
+                closing_price_df["Close"],
+                label=pd.to_datetime(closing_price_df["Date"])
+                .iloc[0]
+                .strftime("%Y-%m-%d"),
+            )
 
-                ax3.set_ylabel("Stock Price")
-                theme.style_primary_axis(ax3)
+            ax3.set_ylabel("Stock Price")
+            theme.style_primary_axis(ax3)
 
-        if external_axes is None:
-            theme.visualize_output()
+    if external_axes is None:
+        theme.visualize_output()
 
     export_data(
         export, os.path.dirname(os.path.abspath(__file__)), "sentiment", df_tweets

openbb_terminal/stocks/due_diligence/fmp_view.py

@@ -3,6 +3,7 @@ __docformat__ = "numpy"
 
 import logging
 import os
+import pandas as pd
 
 from openbb_terminal.decorators import log_start_end
 from openbb_terminal.helper_funcs import export_data, print_rich_table
@@ -34,17 +35,21 @@ def rating(symbol: str, limit: int = 10, export: str = ""):
     """
     df = fmp_model.get_rating(symbol)
 
+    if (isinstance(df, pd.DataFrame) and df.empty) or (
+        not isinstance(df, pd.DataFrame) and not df
+    ):
+        return
+
     # TODO: This could be displayed in a nice rating plot over time
 
-    if not df.empty:
-        df = df.astype(str).applymap(lambda x: add_color(x))
+    df = df.astype(str).applymap(lambda x: add_color(x))
 
-        print_rich_table(
-            df.head(limit),
-            headers=df.columns,
-            show_index=True,
-            title="Rating",
-        )
+    print_rich_table(
+        df.head(limit),
+        headers=df.columns,
+        show_index=True,
+        title="Rating",
+    )
 
     export_data(
         export,