Allow no drawing of CI (#58)

forestplot/arg_validators.py

@@ -14,6 +14,7 @@ def check_data(
     group_order: Optional[Sequence] = None,
     ll: Optional[str] = None,
     hl: Optional[str] = None,
+    form_ci_report: bool = None,
     annote: Optional[Union[Sequence[str], None]] = None,
     annoteheaders: Optional[Union[Sequence[str], None]] = None,
     rightannote: Optional[Union[Sequence[str], None]] = None,
@@ -43,6 +44,8 @@ def check_data(
             Name of column containing the lower limit of the confidence intervals.
     hl (str)
             Name of column containing the upper limit of the confidence intervals.
+    form_ci_report (bool)
+            If True, form the formatted confidence interval as a string.
     annote (list-like)
             List of columns to add as additional annotation in the plot.
     annoteheaders (list-like)
@@ -61,6 +64,24 @@ def check_data(
     -------
             pd.core.frame.DataFrame.
     """
+    ##########################################################################
+    ## Check that CI options (ll, hl, form_ci_report) are consistent
+    ##########################################################################
+    if ll is None:
+        try:
+            assert hl is None
+        except Exception:
+            raise TypeError("'ll' is None. 'hl' should also be None.")
+
+    if hl is None:
+        try:
+            assert ll is None
+        except Exception:
+            raise TypeError("'hl' is None. 'll' should also be None.")
+
+    if ll is None and form_ci_report:
+        warnings.warn("'ll' is None. 'form_ci_report' will be set to False.")
+
     ##########################################################################
     ## Check that numeric data are numeric
     ##########################################################################

forestplot/graph_utils.py

@@ -43,17 +43,18 @@ def draw_ci(
     -------
             Matplotlib Axes object.
     """
-    lw = kwargs.get("lw", 1.4)
-    linecolor = kwargs.get("linecolor", ".6")
-    ax.errorbar(
-        x=dataframe[estimate],
-        y=dataframe[yticklabel],
-        xerr=[dataframe[estimate] - dataframe[ll], dataframe[hl] - dataframe[estimate]],
-        ecolor=linecolor,
-        elinewidth=lw,
-        ls="none",
-        zorder=0,
-    )
+    if ll is not None:
+        lw = kwargs.get("lw", 1.4)
+        linecolor = kwargs.get("linecolor", ".6")
+        ax.errorbar(
+            x=dataframe[estimate],
+            y=dataframe[yticklabel],
+            xerr=[dataframe[estimate] - dataframe[ll], dataframe[hl] - dataframe[estimate]],
+            ecolor=linecolor,
+            elinewidth=lw,
+            ls="none",
+            zorder=0,
+        )
     if logscale:
         ax.set_xscale("log", base=10)
     return ax
@@ -532,6 +533,7 @@ def format_xlabel(xlabel: str, ax: Axes, **kwargs: Any) -> Axes:
 
 def format_xticks(
     dataframe: pd.core.frame.DataFrame,
+    estimate: str,
     ll: str,
     hl: str,
     xticks: Optional[Union[list, range]],
@@ -550,6 +552,9 @@ def format_xticks(
     dataframe (pandas.core.frame.DataFrame)
             Pandas DataFrame where rows are variables. Columns are variable name, estimates,
             margin of error, etc.
+    estimate (str)
+            Name of column containing the estimates (e.g. pearson correlation coefficient,
+            OR, regression estimates, etc.).
     ll (str)
             Name of column containing the lower limit of the confidence intervals.
             Optional
@@ -568,8 +573,12 @@ def format_xticks(
     nticks = kwargs.get("nticks", 5)
     xtick_size = kwargs.get("xtick_size", 10)
     xticklabels = kwargs.get("xticklabels", None)
-    xlowerlimit = dataframe[ll].min()
-    xupperlimit = dataframe[hl].max()
+    if ll is not None:
+        xlowerlimit = dataframe[ll].min()
+        xupperlimit = dataframe[hl].max()
+    else:
+        xlowerlimit = 1.1 * dataframe[estimate].min()
+        xupperlimit = 1.1 * dataframe[estimate].max()
     ax.set_xlim(xlowerlimit, xupperlimit)
     if xticks is not None:
         ax.set_xticks(xticks)
@@ -653,6 +662,7 @@ def draw_tablelines(
     pval: str,
     right_annoteheaders: Optional[Union[Sequence[str], None]],
     ax: Axes,
+    **kwargs: Any
 ) -> Axes:
     """
     Plot horizontal lines as table lines.
@@ -684,7 +694,7 @@ def draw_tablelines(
         [x0, x1], [nrows - 1.45, nrows - 1.45], color="0.5", linewidth=lower_lw, clip_on=False
     )
     if (right_annoteheaders is not None) or (pval is not None):
-        extrapad = 0.05
+        extrapad = kwargs.get("extrapad", 0.05)
         x0 = ax.get_xlim()[1] * (1 + extrapad)
         plt.plot(
             [x0, righttext_width],

forestplot/plot.py

@@ -100,7 +100,7 @@ def forestplot(
     form_ci_report (bool)
             If True, form the formatted confidence interval as a string.
     ci_report (bool)
-            If True, form the formatted confidence interval as a string.
+            If True, report the formatted confidence interval as a string.
     groupvar (str)
             Name of column containing group of variables.
     group_order (list-like)
@@ -170,8 +170,8 @@ def forestplot(
         rightannote=rightannote,
         right_annoteheaders=right_annoteheaders,
     )
-    if (ll is None) or (hl is None):
-        ll, hl = "ll", "hl"
+    if ll is None:
+        ci_report = False
     if ci_report is True:
         form_ci_report = True
     if preprocess:
@@ -371,7 +371,9 @@ def _make_forestplot(
     draw_est_markers(
         dataframe=dataframe, estimate=estimate, yticklabel=yticklabel, ax=ax, **kwargs
     )
-    format_xticks(dataframe=dataframe, ll=ll, hl=hl, xticks=xticks, ax=ax, **kwargs)
+    format_xticks(
+        dataframe=dataframe, estimate=estimate, ll=ll, hl=hl, xticks=xticks, ax=ax, **kwargs
+    )
     draw_ref_xline(
         ax=ax,
         dataframe=dataframe,

forestplot/text_utils.py

@@ -49,16 +49,21 @@ def form_est_ci(
     -------
             pd.core.frame.DataFrame with an additional formatted 'est_ci' column.
     """
-    for col in [estimate, ll, hl]:
+    if ll is None:
+        cols = [estimate]
+    else:
+        cols = [estimate, ll, hl]
+    for col in cols:
         dataframe = _right_justify_num(
             dataframe=dataframe, col=col, decimal_precision=decimal_precision
         )
     for ix, row in dataframe.iterrows():
         formatted_est = row[f"formatted_{estimate}"]
-        formatted_ll, formatted_hl = row[f"formatted_{ll}"], row[f"formatted_{hl}"]
-        formatted_ci = "".join([caps[0], formatted_ll, connector, formatted_hl, caps[1]])
-        dataframe.loc[ix, "ci_range"] = formatted_ci
-        dataframe.loc[ix, "est_ci"] = "".join([formatted_est, formatted_ci])
+        if ll is not None:
+            formatted_ll, formatted_hl = row[f"formatted_{ll}"], row[f"formatted_{hl}"]
+            formatted_ci = "".join([caps[0], formatted_ll, connector, formatted_hl, caps[1]])
+            dataframe.loc[ix, "ci_range"] = formatted_ci
+            dataframe.loc[ix, "est_ci"] = "".join([formatted_est, formatted_ci])
     return dataframe
 
 

tests/test_arg_validators.py

@@ -25,9 +25,9 @@ def test_check_data():
     check_data(dataframe=_df, estimate="estimate", varlabel="varlabel")
 
     # Assert that assertion for numeric type for ll works
-    _df = pd.DataFrame({"estimate": numeric, "ll": string})
+    _df = pd.DataFrame({"estimate": numeric, "ll": string, "hl": numeric})
     with pytest.raises(TypeError) as excinfo:
-        check_data(dataframe=_df, estimate="estimate", varlabel="estimate", ll="ll")
+        check_data(dataframe=_df, estimate="estimate", varlabel="estimate", ll="ll", hl="hl")
     assert str(excinfo.value) == "CI lowerlimit values should be float or int"
 
     # Assert that conversion for numeric ll stored as string works
@@ -41,9 +41,9 @@ def test_check_data():
     check_data(dataframe=_df, estimate="estimate", varlabel="estimate", ll="ll", hl="hl")
 
     # Assert that assertion for numeric type for hl works
-    _df = pd.DataFrame({"estimate": numeric, "hl": string})
+    _df = pd.DataFrame({"estimate": numeric, "ll": numeric, "hl": string})
     with pytest.raises(TypeError) as excinfo:
-        check_data(dataframe=_df, estimate="estimate", varlabel="estimate", hl="hl")
+        check_data(dataframe=_df, estimate="estimate", varlabel="estimate", ll="ll", hl="hl")
     assert str(excinfo.value) == "CI higherlimit values should be float or int"
 
     # Assert that conversion for numeric hl stored as string works
@@ -56,6 +56,17 @@ def test_check_data():
     )
     check_data(dataframe=_df, estimate="estimate", varlabel="estimate", ll="ll", hl="hl")
 
+    # Assert that check for CI options are consistent works
+    _df = pd.DataFrame({"estimate": numeric, "ll": string, "hl": string})
+    with pytest.raises(TypeError) as excinfo:
+        check_data(dataframe=_df, estimate="estimate", varlabel="estimate", ll=None, hl="hl")
+    assert str(excinfo.value) == "'ll' is None. 'hl' should also be None."
+
+    _df = pd.DataFrame({"estimate": numeric, "ll": string, "hl": string})
+    with pytest.raises(TypeError) as excinfo:
+        check_data(dataframe=_df, estimate="estimate", varlabel="estimate", ll="ll", hl=None)
+    assert str(excinfo.value) == "'hl' is None. 'll' should also be None."
+
     ##########################################################################
     ## Check annote
     ##########################################################################

tests/test_graph_utils.py

@@ -230,7 +230,7 @@ def test_format_xticks():
     )
     # No ticks set
     _, ax = plt.subplots()
-    ax = format_xticks(input_df, ll="ll", hl="hl", xticks=None, ax=ax)
+    ax = format_xticks(input_df, estimate="estimate", ll="ll", hl="hl", xticks=None, ax=ax)
     assert isinstance(ax, Axes)
     ax_xmin, ax_xmax = ax.get_xlim()
     data_xmin, data_xmax = input_df.ll.min(), input_df.hl.max()
@@ -239,7 +239,9 @@ def test_format_xticks():
 
     # Set xticks
     _, ax = plt.subplots()
-    ax = format_xticks(input_df, ll="ll", hl="hl", xticks=[1, 2, 3], ax=ax)
+    ax = format_xticks(
+        input_df, estimate="estimate", ll="ll", hl="hl", xticks=[1, 2, 3], ax=ax
+    )
     assert isinstance(ax, Axes)
     ax_xmin, ax_xmax = ax.get_xlim()
     data_xmin, data_xmax = input_df.ll.min(), input_df.hl.max()