[UPDATE] ticks on revenue and ebitda plots

src/plot_utils.py

@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import matplotlib.ticker as ticker
+from matplotlib.ticker import SymmetricalLogLocator as symlog
 import numpy as np
 import pandas as pd
 
@@ -34,7 +35,7 @@ def plotKPIs(fig, ax, data, key, companyMetadata, company, ylabel):
     plt.rcParams['legend.title_fontsize'] = '16'
     return fig
 
-def plotPredictions(fig, ax, company, companyMetadata, time=None, inSamplePrediction=None, outSamplePrediction=None, ground_truth=None, ylabel=None, split=None, plotEverything=False, EBITDA=None):
+def plotPredictions(fig, ax, company, companyMetadata, time=None, inSamplePrediction=None, outSamplePrediction=None, ground_truth=None, ylabel=None, split=None, plotEverything=False):
     size = 22
     if company != "Synspective" and not plotEverything:
         company_color = np.array(companyMetadata[company]["color"]) / 255
@@ -57,14 +58,12 @@ def plotPredictions(fig, ax, company, companyMetadata, time=None, inSamplePredic
         ax.tick_params(axis='both', which='major', labelsize=18)
         ax.set_xlabel('Year', fontsize=size)
         ax.set_ylabel(ylabel, fontsize=size)
+        ax.set_yscale('symlog')
+        vmin, vmax = -10**9, 10**9
+        ax.set_yticks(tick_values(vmin, vmax, 7))
         ax.xaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
-        ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
+        ax.set_ylim([vmin, vmax])
         ax.set_xlim([pd.to_datetime('2018-06-01'), pd.to_datetime('2024-12-31')])
-        if not EBITDA:
-            ax.set_yscale('log')
-            ax.set_ylim([1, 10**9])
-        elif EBITDA:
-            ax.set_ylim([-1.5*10**8, 1.5*10**8])
         ax.plot(time[:-split], inSamplePrediction, color=company_color, linestyle="", marker=".", markersize=18)
         ax.plot(time[-split:], outSamplePrediction, color=company_color, marker="*", markersize=18, linestyle="")
         ax.plot(time, ground_truth, color=company_color, linestyle="solid", linewidth=12)
@@ -85,4 +84,77 @@ def plotPredictions(fig, ax, company, companyMetadata, time=None, inSamplePredic
         # Add the legend with specified labels
         fig.legend([line1, line2, line3], ['In-Sample Prediction', 'Out-of-Sample Forecast', 'Ground Truth Data'],
            loc='upper center', bbox_to_anchor=(0.5, 0.05), ncol=3, prop={'size': size})
-    return fig
+    return fig
+
+# Fixes setting number of ticks on a symlog scale, copied from:
+# https://github.com/matplotlib/matplotlib/issues/17402
+def tick_values(vmin, vmax, numticks):
+    base = 10
+    linthresh = 10
+
+    if vmax < vmin:
+        vmin, vmax = vmax, vmin
+
+    # "simple" mode is when the range falls entirely within (-t,
+    # t) -- it should just display (vmin, 0, vmax)
+    if -linthresh < vmin < vmax < linthresh:
+        # only the linear range is present
+        return [vmin, vmax]
+
+    # Lower log range is present
+    has_a = (vmin < -linthresh)
+    # Upper log range is present
+    has_c = (vmax > linthresh)
+
+    # Check if linear range is present
+    has_b = (has_a and vmax > -linthresh) or (has_c and vmin < linthresh)
+
+    def get_log_range(lo, hi):
+        lo = np.floor(np.log(lo) / np.log(base))
+        hi = np.ceil(np.log(hi) / np.log(base))
+        return lo, hi
+
+    # Calculate all the ranges, so we can determine striding
+    a_lo, a_hi = (0, 0)
+    if has_a:
+        a_upper_lim = min(-linthresh, vmax)
+        a_lo, a_hi = get_log_range(abs(a_upper_lim), abs(vmin) + 1)
+
+    c_lo, c_hi = (0, 0)
+    if has_c:
+        c_lower_lim = max(linthresh, vmin)
+        c_lo, c_hi = get_log_range(c_lower_lim, vmax + 1)
+
+    # Calculate the total number of integer exponents in a and c ranges
+    total_ticks = (a_hi - a_lo) + (c_hi - c_lo)
+    if has_b:
+        total_ticks += 1
+    stride = max(total_ticks // (numticks - 1), 1)
+
+    decades = []
+    if has_a:
+        decades.extend(-1 * (base ** (np.arange(a_lo, a_hi, stride)[::-1])))
+
+    if has_b:
+        decades.append(0.0)
+
+    if has_c:
+        decades.extend(base ** (np.arange(c_lo, c_hi, stride)))
+
+    subs = np.arange(1.0, base)
+
+    if len(subs) > 1 or subs[0] != 1.0:
+        ticklocs = []
+        for decade in decades:
+            if decade == 0:
+                ticklocs.append(decade)
+            else:
+                ticklocs.extend(subs * decade)
+    else:
+        ticklocs = decades
+        # if there is not enough ticks to show on the graph
+        if len(ticklocs) <= 3:
+            ticklocs.extend(subs)
+            subs= np.linspace(vmin, vmax, 10) # 7 can be changed, it will just change the number of value displayed
+
+    return np.array(ticklocs)

src/process.py

@@ -227,11 +227,11 @@ def generate_next_quarters(start_date, n):
         plot_pacf(transformedData["Revenue_USD"], lags=2, title=company, ax = ax28[i//3, i%3], color = 'red')
         plot_acf(transformedData["EBITDA_USD"], lags=3, title=company, ax = ax29[i//3, i%3], color = 'red')
         plot_pacf(transformedData["EBITDA_USD"], lags=2, title=company, ax = ax30[i//3, i%3], color = 'red')
-        plotPredictions(fig17, ax17[i], company, companyMetadata, filteredData["Quarter"], inSamplePredictions1, outSamplePredictions1, transformedData["Revenue_USD"], "Revenue $ (USD)", split, plotEverything=True, EBITDA=False)
-        plotPredictions(fig18, ax18[i], company, companyMetadata, filteredData["Quarter"], inSamplePredictions2, outSamplePredictions2, transformedData["EBITDA_USD"], "EBITDA $ (USD)", split, plotEverything=True, EBITDA=True)
+        plotPredictions(fig17, ax17[i], company, companyMetadata, filteredData["Quarter"], inSamplePredictions1, outSamplePredictions1, transformedData["Revenue_USD"], "Revenue $ (USD)", split, plotEverything=True)
+        plotPredictions(fig18, ax18[i], company, companyMetadata, filteredData["Quarter"], inSamplePredictions2, outSamplePredictions2, transformedData["EBITDA_USD"], "EBITDA $ (USD)", split, plotEverything=True)
     else:
-        plotPredictions(fig17, ax17[i], company, companyMetadata, plotEverything=True, EBITDA=False)
-        plotPredictions(fig18, ax18[i], company, companyMetadata, plotEverything=True, EBITDA=True)
+        plotPredictions(fig17, ax17[i], company, companyMetadata, plotEverything=True)
+        plotPredictions(fig18, ax18[i], company, companyMetadata, plotEverything=True)
 
     plot_acf(transformedData["Total_Assets_USD"], lags=3, title=company, ax = ax21[i//3, i%3], color = 'red')
     plot_pacf(transformedData["Total_Assets_USD"], lags=2, title=company, ax = ax22[i//3, i%3], color = 'red')