ticker | An Android text view with scrolling text change animation | Animation library

by robinhood Java Version: 2.0.2 License: Apache-2.0

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kandi X-RAY | ticker Summary

ticker is a Java library typically used in User Interface, Animation applications. ticker has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub.

Ticker is a simple Android UI component for displaying scrolling text. Think about how an odometer scrolls when going from one number to the next, that is similar to what Ticker does. The Ticker handles smooth animations between strings and also string resizing (e.g. animate from "9999" to "10000"). You can specify how the animations proceed by defining an array of characters in order. Each character displayed by Ticker is controlled by this array which dictates how to animate from a starting character to a target character. For example, if you just use a basic ASCII character list, when animating from 'A' to 'Z', it will go from 'A' → 'B' → … 'Z'. We will perform wrap-around animation when it’s faster (e.g. 'Z' to 'A' will just animate 'Z' → 'A').

Support

Quality

Security

License

Reuse

kandi-support Support

ticker has a highly active ecosystem.
It has 4111 star(s) with 434 fork(s). There are 87 watchers for this library.
It had no major release in the last 12 months.
There are 14 open issues and 61 have been closed. On average issues are closed in 103 days. There are no pull requests.
It has a positive sentiment in the developer community.
The latest version of ticker is 2.0.2

ticker Support

Best in #Animation

Average in #Animation

ticker Support

Best in #Animation

Average in #Animation

quality kandi Quality

ticker has 0 bugs and 0 code smells.

ticker Quality

Best in #Animation

Average in #Animation

ticker Quality

Best in #Animation

Average in #Animation

security Security

ticker has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
ticker code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

ticker Security

Best in #Animation

Average in #Animation

ticker Security

Best in #Animation

Average in #Animation

license License

ticker is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

ticker License

Best in #Animation

Average in #Animation

ticker License

Best in #Animation

Average in #Animation

build Reuse

ticker releases are available to install and integrate.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
ticker saves you 766 person hours of effort in developing the same functionality from scratch.
It has 1763 lines of code, 162 functions and 28 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.

ticker Reuse

Best in #Animation

Average in #Animation

ticker Reuse

Best in #Animation

Average in #Animation

Top functions reviewed by kandi - BETA

kandi has reviewed ticker and discovered the below as its top functions. This is intended to give you an instant insight into ticker implemented functionality, and help decide if they suit your requirements.

Initialize the view .
Append the column actions for the given substring .
Set the current character progress .
Gets the character indices of the character in the specified range .
Get the character width
Returns a runnable that returns a runnable that executes when the update is pressed .
Initializes the activity .
Updates the ticker
Returns the current text .
Provide alphabetically sorted alphabetically

ticker Key Features

There are some breaking API changes introduced in ticker 2.0. Please refer to the [2.0 migration doc](https://github.com/robinhood/ticker/blob/master/2_0_migration.md).

ticker Examples and Code Snippets

See all related Code Snippets

Getting started

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implementation 'com.robinhood.ticker:ticker:2.0.2'

Usage

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&lt;com.robinhood.ticker.TickerView
    android:id="@+id/tickerView"
    android:layout_width="wrap_content"
    android:layout_height="wrap_content" /&gt;

Customization

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android:gravity="center"
android:textColor="@color/colorPrimary"
android:textSize="16sp"
app:ticker_animationDuration="1500"
app:ticker_preferredScrollingDirection="any"

License

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Copyright 2016 Robinhood Markets, Inc.

How to unlist a list and keep the names of the top level as a new variable in R

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data:table::rbindlist(my_list, idcol = "Ticker")

library(tidyverse)

my_list %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  unnest(cols = c(date, Value))

#  A tibble: 30 x 3
   name    date       Value
   <chr>   <date>     <int>
 1 Ticker1 2021-01-01     1
 2 Ticker1 2021-01-02     2
 3 Ticker1 2021-01-03     3
 4 Ticker1 2021-01-04     4
 5 Ticker1 2021-01-05     5
 6 Ticker1 2021-01-06     6
 7 Ticker1 2021-01-07     7
 8 Ticker1 2021-01-08     8
 9 Ticker1 2021-01-09     9
10 Ticker1 2021-01-10    10
# ... with 20 more rows

-----------------------

data:table::rbindlist(my_list, idcol = "Ticker")

library(tidyverse)

my_list %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  unnest(cols = c(date, Value))

#  A tibble: 30 x 3
   name    date       Value
   <chr>   <date>     <int>
 1 Ticker1 2021-01-01     1
 2 Ticker1 2021-01-02     2
 3 Ticker1 2021-01-03     3
 4 Ticker1 2021-01-04     4
 5 Ticker1 2021-01-05     5
 6 Ticker1 2021-01-06     6
 7 Ticker1 2021-01-07     7
 8 Ticker1 2021-01-08     8
 9 Ticker1 2021-01-09     9
10 Ticker1 2021-01-10    10
# ... with 20 more rows

-----------------------

library(dplyr)

bind_rows(my_list, .id = "Ticker")

# A tibble: 30 x 3
   Ticker  date       Value
   <chr>   <date>     <int>
 1 Ticker1 2021-01-01     1
 2 Ticker1 2021-01-02     2
 3 Ticker1 2021-01-03     3
 4 Ticker1 2021-01-04     4
 5 Ticker1 2021-01-05     5
 6 Ticker1 2021-01-06     6
 7 Ticker1 2021-01-07     7
 8 Ticker1 2021-01-08     8
 9 Ticker1 2021-01-09     9
10 Ticker1 2021-01-10    10
# ... with 20 more rows

-----------------------

library(dplyr)

bind_rows(my_list, .id = "Ticker")

# A tibble: 30 x 3
   Ticker  date       Value
   <chr>   <date>     <int>
 1 Ticker1 2021-01-01     1
 2 Ticker1 2021-01-02     2
 3 Ticker1 2021-01-03     3
 4 Ticker1 2021-01-04     4
 5 Ticker1 2021-01-05     5
 6 Ticker1 2021-01-06     6
 7 Ticker1 2021-01-07     7
 8 Ticker1 2021-01-08     8
 9 Ticker1 2021-01-09     9
10 Ticker1 2021-01-10    10
# ... with 20 more rows

-----------------------

do.call(rbind, lapply(my_list, data.frame))
#                  date Value
# Ticker1.1  2021-01-01     1
# Ticker1.2  2021-01-02     2
# Ticker1.3  2021-01-03     3
# Ticker1.4  2021-01-04     4
# Ticker1.5  2021-01-05     5
# ...

do.call(rbind, 
        lapply(names(my_list), function(i){
          data.frame(Ticker = i, my_list[[ i ]])
        }))
#     Ticker       date Value
# 1  Ticker1 2021-01-01     1
# 2  Ticker1 2021-01-02     2
# 3  Ticker1 2021-01-03     3
# 4  Ticker1 2021-01-04     4
# 5  Ticker1 2021-01-05     5
# ...

-----------------------

do.call(rbind, lapply(my_list, data.frame))
#                  date Value
# Ticker1.1  2021-01-01     1
# Ticker1.2  2021-01-02     2
# Ticker1.3  2021-01-03     3
# Ticker1.4  2021-01-04     4
# Ticker1.5  2021-01-05     5
# ...

do.call(rbind, 
        lapply(names(my_list), function(i){
          data.frame(Ticker = i, my_list[[ i ]])
        }))
#     Ticker       date Value
# 1  Ticker1 2021-01-01     1
# 2  Ticker1 2021-01-02     2
# 3  Ticker1 2021-01-03     3
# 4  Ticker1 2021-01-04     4
# 5  Ticker1 2021-01-05     5
# ...

Error: Property 'body' cannot be accessed on 'Response?' because it is potentially null

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if (statusCode == 200) {
  return Text(
 "${BTCPrice.fromJson(jsonDecode(snapshot.data!.body)).eur}"); // adding ! on data
}

final response = await http.get(Uri.https('blockhain.info', 'ticker'));

final response = await http.get(Uri.parse('https://blockchain.info/ticker'));

-----------------------

if (statusCode == 200) {
  return Text(
 "${BTCPrice.fromJson(jsonDecode(snapshot.data!.body)).eur}"); // adding ! on data
}

final response = await http.get(Uri.https('blockhain.info', 'ticker'));

final response = await http.get(Uri.parse('https://blockchain.info/ticker'));

-----------------------

if (statusCode == 200) {
  return Text(
 "${BTCPrice.fromJson(jsonDecode(snapshot.data!.body)).eur}"); // adding ! on data
}

final response = await http.get(Uri.https('blockhain.info', 'ticker'));

final response = await http.get(Uri.parse('https://blockchain.info/ticker'));

HTML DOM id Array Property & Adding to Stage JavaScript (Array)

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<html>
    <script type="text/javascript" src="https://code.createjs.com/1.0.0/createjs.min.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
    <style>
        body { margin: 0; }
        #gameCanvas { background-color: lightblue; }
        .canvasHolder1 { position: relative; }
        .ui { position: absolute; top: 0; left: 0;  pointer-events: none;}
        .ui-element { position: relative; display: inline-block; margin-left: 4px; }
    </style>
    <body onload="startGame();">
        <div class="canvasHolder1">
            <div id="ship">⛵ </div>
            <div class="ui"></div>
            <canvas id="gameCanvas" width="300" height="300">Not supported</canvas>
        </div>

        <script type="text/javascript">
            const stage = new createjs.Stage("gameCanvas");
            const ship = document.getElementById("ship");
            const ui = document.querySelector(".ui");
            const canvas = document.getElementById("gameCanvas");
            const canvasHolder = document.querySelector('.canvasHolder1');

            var divs = [];
            const texts = [ 'My Ship', 'Pos X', 'Pos Y', 'Other' ];

            for (i = 0; i <= 3; i++) {
                const newDiv = document.createElement("type");
                newDiv.id = `q${i}`;
                newDiv.className = `ui-element`;
                newDiv.innerHTML = texts[i];
                ui.appendChild(newDiv);
                divs.push(newDiv);
            }


            const textXPosition = divs[1];
            const textYPosition = divs[2];
            const textProgress = divs[3];

            canvas.addEventListener('mousemove', (evt) => {
                textXPosition.innerHTML = evt.pageX;
                textYPosition.innerHTML = evt.pageY;
            });


            var objectArray = [];
            objectArray.push(new createjs.DOMElement(divs[0]));
            const myFirstText = objectArray[0];
            const myFirstTextWidth = parseInt(myFirstText.htmlElement.offsetWidth);

            var shipJS = new createjs.DOMElement(ship);

            function applyPosition(posObject) {
                shipJS.x = posObject.x;
                shipJS.y = posObject.y;

                myFirstText.x = posObject.x - (myFirstTextWidth / 2);
                myFirstText.y = posObject.y - 15;
            }

            const shipPosition = {
                x: canvas.width / 3,
                y: 50
            };
            applyPosition(shipPosition);


            let looperCount = -1;
            const step = 0.02;
            const distance = canvas.width / 3;

            function handleTick(event) {
                if (looperCount >= 1) { looperCount = -1; }
                looperCount += step;
                const progress = Math.PI * looperCount;
                textProgress.innerHTML = parseInt(((looperCount + 1) / 2) * 100 )+'%';

                applyPosition({
                    x: (Math.cos(progress) * distance) + (distance * 1.5),
                    y: (Math.sin(progress) * distance) + (distance * 1.5)
                });
                stage.update();
            }


            function startGame() {
                stage.addChild(shipJS);
                stage.addChild(myFirstText);

                createjs.Ticker.addEventListener("tick", handleTick);

            }
        </script>
    </body>
</html>

Generate a progressive bar chart within table (like in Excel)?

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library(gt)
# remotes::install_github("jthomasmock/gtExtras")
library(gtExtras)

df <- structure(list(ticker = c("AAPL", "MSFT", "AMZN", "NVDA"), 
                     high = c("182.94", "349.67", "3,773.08", "346.47"), 
                     current = c(170.7, 308, 2885, 231.4)))
df <- as.data.frame(df)
df$high <- gsub(",", "", df$high)
df$high <- as.numeric(df$high)
df$Off_by <- round((df$high - df$current) /df$current, 3) 

gt::gt(df) %>%
  gtExtras::gt_plt_bar(column = Off_by, keep_column = TRUE, color = "red", scale_type = "percent")

-----------------------

library(kableExtra)
library(tidyverse)

df <- structure(list(ticker = c("AAPL", "MSFT", "AMZN", "NVDA"), high = c("182.94", 
                                                                          "349.67", "3,773.08", "346.47"), current = c(170.7, 308, 2885, 
                                                                                                                       231.4), Off_by = c(-7, -14, -31, -50)), class = "data.frame", row.names = c(NA, 
                                                                                                                                                                                                   -4L))

## Need a hacky factor for making the bars long enough to cover the labels
hack <- 2.5
## that's for the gradient, inspired by Alan https://stackoverflow.com/a/61777415/7941188
grad_df <- data.frame(
  xintercept = seq(-100 * hack, 0, length.out = 2000),
  alpha = seq(0.3, 0, length.out = 2000)
)
inline_bars <-
  df %>%
  ## need to make sure the plots are in the right order
  mutate(ticker = fct_inorder(ticker)) %>%
  group_split(ticker) %>%
  map(~ ggplot(.x, aes(x = Off_by * hack, y = "")) +
    geom_col(fill = "darkred") +
    geom_text(aes(x = 0, label = paste0(Off_by, "%")),
      hjust = 1, color = "white",
      ## the size is chosen because you will have 50points, and this will just nearly
      ## fill an inch (your chosen file size)
      size = 5 * 50 / 14
    ) +
    ## important to have the same limits for every plot
    coord_cartesian(xlim = c(-100, 0), clip = "off") +
    ## for the gradient
    geom_vline(
      data = grad_df, mapping = aes(xintercept = xintercept, alpha = alpha),
      color = "white"
    ) +
    theme_void() +
    theme(legend.position = "none"))

map(1:4, function(i) {
  ggsave(
    filename = paste0(df$ticker[i], ".png"),
    plot = inline_bars[[i]], height = 1, dpi = 300
  )
})

ls_inline_plots <- file.path(getwd(), paste0(df$ticker, ".png"))

df %>%
  ## remove cell content for the plot
  mutate(Off_by = "") %>%
  kbl(booktabs = TRUE) %>%
  kable_paper(full_width = FALSE) %>%
  column_spec(4, image = spec_image(ls_inline_plots, width = 200, height = 50)) %>%
  ## RStudio preview does not always show the images, but the final html file shows them
  cat(., file = "tbl.html")

Vectorize for loop and return x day high and low

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# number of days
N = 8

df.join(df.rolling(f'{N+1}d', min_periods=1)
          .agg({'High': lambda s: s.le(s.iloc[-1])[::-1].cumprod().sum()-1,
                'Low': lambda s: s.ge(s.iloc[-1])[::-1].cumprod().sum()-1,
               })
          .add_prefix(f'{N}_days_')
        )

              Open    High     Low   Close  8_days_High  8_days_Low
date                                                               
2020-03-19  1.1904  1.1937  1.1832  1.1832          0.0         0.0
2020-03-20  1.1843  1.1879  1.1769  1.1772          0.0         1.0
2020-03-21  1.1755  1.1767  1.1662  1.1672          0.0         2.0
2020-03-22  1.1686  1.1721  1.1611  1.1721          0.0         3.0
2020-03-23  1.1732  1.1819  1.1690  1.1819          2.0         0.0
2020-03-24  1.1836  1.1928  1.1807  1.1922          4.0         0.0
2020-03-25  1.1939  1.1939  1.1864  1.1936          6.0         0.0
2020-03-26  1.1967  1.2141  1.1964  1.2114          7.0         0.0
2020-03-27  1.2118  1.2144  1.2039  1.2089          7.0         0.0
2020-03-28  1.2080  1.2099  1.2018  1.2041          0.0         1.0
2020-03-29  1.2033  1.2033  1.1853  1.1880          0.0         4.0
2020-03-30  1.1876  1.1887  1.1806  1.1879          0.0         6.0
2020-03-31  1.1921  1.1972  1.1873  1.1939          1.0         0.0
2020-04-01  1.1932  1.1997  1.1914  1.1914          2.0         0.0
2020-04-02  1.1902  1.1924  1.1781  1.1862          0.0         7.0

-----------------------

# number of days
N = 8

df.join(df.rolling(f'{N+1}d', min_periods=1)
          .agg({'High': lambda s: s.le(s.iloc[-1])[::-1].cumprod().sum()-1,
                'Low': lambda s: s.ge(s.iloc[-1])[::-1].cumprod().sum()-1,
               })
          .add_prefix(f'{N}_days_')
        )

              Open    High     Low   Close  8_days_High  8_days_Low
date                                                               
2020-03-19  1.1904  1.1937  1.1832  1.1832          0.0         0.0
2020-03-20  1.1843  1.1879  1.1769  1.1772          0.0         1.0
2020-03-21  1.1755  1.1767  1.1662  1.1672          0.0         2.0
2020-03-22  1.1686  1.1721  1.1611  1.1721          0.0         3.0
2020-03-23  1.1732  1.1819  1.1690  1.1819          2.0         0.0
2020-03-24  1.1836  1.1928  1.1807  1.1922          4.0         0.0
2020-03-25  1.1939  1.1939  1.1864  1.1936          6.0         0.0
2020-03-26  1.1967  1.2141  1.1964  1.2114          7.0         0.0
2020-03-27  1.2118  1.2144  1.2039  1.2089          7.0         0.0
2020-03-28  1.2080  1.2099  1.2018  1.2041          0.0         1.0
2020-03-29  1.2033  1.2033  1.1853  1.1880          0.0         4.0
2020-03-30  1.1876  1.1887  1.1806  1.1879          0.0         6.0
2020-03-31  1.1921  1.1972  1.1873  1.1939          1.0         0.0
2020-04-01  1.1932  1.1997  1.1914  1.1914          2.0         0.0
2020-04-02  1.1902  1.1924  1.1781  1.1862          0.0         7.0

-----------------------

@numba.njit
def count_smaller(arr):
    current = arr[-1]
    count = 0
    
    for i in range(arr.shape[0]-2, -1, -1):
        if arr[i] > current:
            break
        
        count += 1
        
    return count


@numba.njit
def count_greater(arr):
    current = arr[-1]
    count = 0
    
    for i in range(arr.shape[0]-2, -1, -1):
        if arr[i] < current:
            break
        
        count += 1
        
    return count

df["x_day_high"] = df.High.expanding().apply(count_smaller, engine='numba', raw=True)
df["x_day_low"] = df.Low.expanding().apply(count_greater, engine='numba', raw=True)

def count_consecutive_true(bool_arr):
    return bool_arr[::-1].cumprod().sum()

def count_smaller(arr):
    return count_consecutive_true(arr <= arr[-1]) - 1

def count_greater(arr):
    return count_consecutive_true(arr >= arr[-1]) - 1

df["x_day_high"] = df.High.expanding().apply(count_smaller, raw=True)
df["x_day_low"] = df.Low.expanding().apply(count_greater, raw=True)

-----------------------

@numba.njit
def count_smaller(arr):
    current = arr[-1]
    count = 0
    
    for i in range(arr.shape[0]-2, -1, -1):
        if arr[i] > current:
            break
        
        count += 1
        
    return count


@numba.njit
def count_greater(arr):
    current = arr[-1]
    count = 0
    
    for i in range(arr.shape[0]-2, -1, -1):
        if arr[i] < current:
            break
        
        count += 1
        
    return count

df["x_day_high"] = df.High.expanding().apply(count_smaller, engine='numba', raw=True)
df["x_day_low"] = df.Low.expanding().apply(count_greater, engine='numba', raw=True)

def count_consecutive_true(bool_arr):
    return bool_arr[::-1].cumprod().sum()

def count_smaller(arr):
    return count_consecutive_true(arr <= arr[-1]) - 1

def count_greater(arr):
    return count_consecutive_true(arr >= arr[-1]) - 1

df["x_day_high"] = df.High.expanding().apply(count_smaller, raw=True)
df["x_day_low"] = df.Low.expanding().apply(count_greater, raw=True)

How to make labels appear when clicking on a step plot subplot point in matplotlib (or possibly plotly)?

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import pandas as pd
import numpy as np
import matplotlib as mtpl
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker

annotations = []
data = {
    # 'Name': ['Status', 'Status', 'HMI', 'Allst', 'Drvr', 'CurrTUBand', 'RUSource', 'RUReqstrPriority', 'RUReqstrSystem', 'RUResReqstStat', 'CurrTUBand', 'DSP', 'SetDSP', 'SetDSP', 'DSP', 'RUSource', 'RUReqstrPriority', 'RUReqstrSystem', 'RUResReqstStat', 'Status', 'Delay', 'Status', 'Delay', 'HMI', 'Status', 'Status', 'HMI', 'DSP'],
    # 'Value': [4, 4, 2, 1, 1, 1, 0, 7, 0, 4, 1, 1, 3, 0, 3, 0, 7, 0, 4, 1, 0, 1, 0, 1, 4, 4, 2, 3],
    # 'Gage': ['H1', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H1', 'H1', 'H3', 'H3', 'H3', 'H1', 'H3', 'H3', 'H3'],
    # 'Id_Par': [0, 0, 0, 0, 0, 0, 10, 10, 10, 10, 10, 0, 0, 22, 22, 28, 28, 28, 28, 0, 0, 38, 38, 0, 0, 0, 0, 0]
    'Name': ['Lamp_D_Rq', 'Status', 'Status', 'HMI', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lamp_D_Rq', 'Lamp_D_Rq', 'Lamp_D_Rq', 'Lamp_D_Rq'],
    'Value': [0, 4, 4, 2, 1, 1, 2, 2, 1, 1, 3, 3],
    'Gage': ['F1', 'H1', 'H3', 'H3', 'H3', 'F1', 'H3', 'F1', 'F1', 'H3', 'F1', 'H3'],
    'Id_Par': [0, 0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0]
    }

signals_df = pd.DataFrame(data)


def plot_signals(signals_df):
    print(signals_df)
    # Count signals by parallel
    signals_df['Count'] = signals_df.groupby('Id_Par').cumcount().add(1).mask(signals_df['Id_Par'].eq(0), 0)
    # Subtract Parallel values from the index column
    signals_df['Sub'] = signals_df.index - signals_df['Count']
    id_par_prev = signals_df['Id_Par'].unique()
    id_par = np.delete(id_par_prev, 0)
    signals_df['Prev'] = [1 if x in id_par else 0 for x in signals_df['Id_Par']]
    signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
    # Convert and set Subtract to index
    signals_df.set_index('Final', inplace=True)

    # Get individual names and variables for the chart
    names_list = [name for name in signals_df['Name'].unique()]
    num_names_list = len(names_list)
    num_axisx = len(signals_df["Name"])

    # Matplotlib's categorical feature to convert x-axis values to string
    x_values = [-1, ]
    x_values += (list(set(signals_df.index)))
    x_values = [str(i) for i in sorted(x_values)]

    # Creation Graphics
    fig, ax = plt.subplots(nrows=num_names_list, figsize=(10, 10), sharex=True)
    plt.xticks(np.arange(0, num_axisx), color='SteelBlue', fontweight='bold')

    # Loop to build the different graphs
    for pos, name in enumerate(names_list):
        print("name: %s" % name)
        print("pos: %s" % pos)
        # Creating a dummy plot and then remove it
        dummy, = ax[pos].plot(x_values, np.zeros_like(x_values))
        dummy.remove()

        # Get names by values and gage data
        data = signals_df[signals_df["Name"] == name]["Value"]
        data_gage = signals_df[signals_df["Name"] == name]["Gage"]

        # Get values axis-x and axis-y
        x_ = np.hstack([-1, data.index.values, len(signals_df) - 1])
        y_ = np.hstack([0, data.values, data.iloc[-1]])
        y_gage = np.hstack(["", "-", data_gage.values])
        # print(y_gage)

        # Plotting the data by position
        steps = ax[pos].plot(x_.astype('str'), y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
        ax[pos].set_ylabel(name, fontsize=8, fontweight='bold', color='SteelBlue', rotation=30, labelpad=35)
        ax[pos].yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
        ax[pos].yaxis.set_tick_params(labelsize=6)
        ax[pos].grid(alpha=0.4, color='SteelBlue')
        # Labeling the markers with Values and Gage
        xy_temp = []
        for i in range(len(y_)):
            if i == 0:
                xy = [x_[0].astype('str'), y_[0]]
                xy_temp.append(xy)
            else:
                xy = [x_[i - 1].astype('str'), y_[i - 1]]
                xy_temp.append(xy)

            # Creating values in text inside the plot
            ax[pos].text(x=xy[0], y=xy[1], s=str(xy[1]), color='k', fontweight='bold', fontsize=12)

            for val_gage, xy in zip(y_gage, xy_temp):
                print("val_gage: %s" % val_gage)
                annot = ax[pos].annotate(val_gage, xy=xy, xytext=(-20, 20), textcoords="offset points",
                                         bbox=dict(boxstyle="round", fc="w"),
                                         arrowprops=dict(arrowstyle="->"))

                annot.set_visible(False)
                annotations.append(annot)

    # Function for storing and showing the clicked values
    def update_annot(ind):
        print("Enter update_annot")
        coord = steps[0].get_offsets()[ind["ind"][0]]
        annot.xy = coord
        text = "{}, {}".format(" ".join(list(map(str, ind["ind"]))),
                                " ".join([y_gage[n] for n in ind["ind"]]))
        annot.set_text(text)
        annot.get_bbox_patch().set_alpha(0.4)

    def on_click(event):
        print("Enter on_click")
        vis = annot.get_visible()
        # make the first three annotations visible
        for i in range(0, 3):
            print('elem visible')
            annotations[i].set_visible(True)
        print(event.inaxes)
        print(ax[pos])
        print(event.inaxes == ax[pos])
        if event.inaxes == ax[pos]:
            cont, ind = steps[0].contains(event)
            print (ind)
            if cont:
                update_annot(ind)
                annot.set_visible(True)
                fig.canvas.draw_idle()
            else:
                if vis:
                    annot.set_visible(False)
                    fig.canvas.draw_idle()

    fig.canvas.mpl_connect("button_press_event",on_click)

    plt.show()

plot_signals(signals_df)

-----------------------

import plotly.express as px
import plotly.graph_objs as go

import pandas as pd
import numpy as np

data = {
    "Name": [
        "Lamp_D_Rq", "Status", "Status", "HMI",
        "Lck_D_RqDrv3", "Lck_D_RqDrv3", "Lck_D_RqDrv3",
        "Lck_D_RqDrv3", "Lamp_D_Rq", "Lamp_D_Rq",
        "Lamp_D_Rq", "Lamp_D_Rq",
    ],
    "Value": [0, 4, 4, 2, 1, 1, 2, 2, 1, 1, 3, 3],
    "Gage": [
        "F1", "H1", "H3", "H3", "H3",
        "F1", "H3", "F1", "F1", "H3",
        "F1", "H3",
    ],
    "Id_Par": [0, 0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0],
}

signals_df = pd.DataFrame(data)

fig = px.line(
    signals_df,
    y="Value",
    x="Sub",
    color="Name",
    hover_data=["Gage"],
    custom_data=["Gage"],
    markers=True,
    height=500,
    render_mode="svg")

fig.update_traces(line={"shape": 'hv'})
fig.update_traces(
    hovertemplate="<br>".join([
        "Gage: %{customdata[0]}",
    ])
)
fig.show(config={'displaylogo': False})

-----------------------

import plotly.express as px
import plotly.graph_objs as go

import pandas as pd
import numpy as np

data = {
    "Name": [
        "Lamp_D_Rq", "Status", "Status", "HMI",
        "Lck_D_RqDrv3", "Lck_D_RqDrv3", "Lck_D_RqDrv3",
        "Lck_D_RqDrv3", "Lamp_D_Rq", "Lamp_D_Rq",
        "Lamp_D_Rq", "Lamp_D_Rq",
    ],
    "Value": [0, 4, 4, 2, 1, 1, 2, 2, 1, 1, 3, 3],
    "Gage": [
        "F1", "H1", "H3", "H3", "H3",
        "F1", "H3", "F1", "F1", "H3",
        "F1", "H3",
    ],
    "Id_Par": [0, 0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0],
}

signals_df = pd.DataFrame(data)

fig = px.line(
    signals_df,
    y="Value",
    x="Sub",
    color="Name",
    hover_data=["Gage"],
    custom_data=["Gage"],
    markers=True,
    height=500,
    render_mode="svg")

fig.update_traces(line={"shape": 'hv'})
fig.update_traces(
    hovertemplate="<br>".join([
        "Gage: %{customdata[0]}",
    ])
)
fig.show(config={'displaylogo': False})

Confidence Interval 3 dimensional plot

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import plotly.graph_objects as go
import numpy as np

x = np.arange(0.1,1.1,0.01)
y = np.linspace(-np.pi,np.pi,100)

X,Y = np.meshgrid(x,y)
result = []
for i,j in zip(X,Y):
    result.append(np.log(i)+np.sin(j))

upper_bound = np.array(result)+1
lower_bound = np.array(result)-1

fig = go.Figure(data=[
    go.Surface(z=result),
    go.Surface(z=upper_bound, showscale=False, opacity=0.3,colorscale='purp'),
    go.Surface(z=lower_bound, showscale=False, opacity=0.3,colorscale='purp'),

])
fig.show()

f-string in yaxis.set_major_formatter

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ax.yaxis.set_major_formatter(c + '{x:1.2f}')

ax.yaxis.set_major_formatter(f'{c}{{x:1.2f}}')

-----------------------

ax.yaxis.set_major_formatter(c + '{x:1.2f}')

ax.yaxis.set_major_formatter(f'{c}{{x:1.2f}}')

Pandas conditional formatting not displaying background colors. (no errors)

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return 'background-color: %s' % background_color

import numpy as np


def color_negative_red(subset_df):
    # Create Styles based on multiple conditions with np.select
    return np.select(
        [subset_df < 0, subset_df > 0],
        ['background-color: red', 'background-color: green'],
        default=''
    )


# Select Subset of columns
cols = df.columns[df.columns.str.endswith('%')]
# This can also be hard-coded
# cols = ['Daily%', 'WTD%', 'MTD%', 'QTD%', 'YTD%']
(
    df.style.apply(
        color_negative_red, subset=cols, axis=None
    ).format(
        # This can be used to set general number styles without a fmt string
        precision=2, thousands=",", subset='Close'
    ).format(
        # Apply percent format to subset of cols
        '{:,.2f}%', subset=cols
    ).hide_index()  # To remove the "row numbers" hide the index
)

df = pd.DataFrame({'Stock': ['SPY', 'TLT', 'XLY', 'XLF', 'XLV', 'XLK', 'XLP', 'XLI', 'XLB', 'XLE', 'XLU', 'XLRE', 'XLC'], 'Close': [453.0799865722656, 148.17999267578125, 183.52000427246094, 38.06999969482422, 136.85000610351562, 159.36000061035156, 72.83000183105469, 104.58999633789062, 85.37000274658203, 48.619998931884766, 69.48999786376953, 48.72999954223633, 85.47000122070312], 'Daily%': [-0.024275881759957655, -0.9094561130207558, -0.05990339673614984, -0.6264726438619272, 0.10973603410557096, 0.39690348375861295, -0.12341791024475374, -0.6081982311376585, -0.66325305868224, -0.5726024238382843, -0.8277463016708558, 0.0, 0.0], 'WTD%': [0.18795205211694732, -1.1144566965053964, 0.1692033989594774, -0.9882992472694656, 1.078371165114885, -0.2128968969240593, 1.0124873403369272, -0.20038807991586702, -0.5706939905500718, -0.22573488894909668, 1.2678447717357961, 2.8059028977694966, 0.19929444649862676], 'MTD%': [0.28331093711392863, -0.47686662420427856, -0.06534258200082742, -0.28811055388244855, 1.1829989674792052, 0.2894944817725163, 0.26156723981653324, 0.4610467051436526, -0.023418015235201397, 1.9500950554754906, 0.07199801354481171, 0.4742258602810889, -0.6740288326379547], 'QTD%': [5.262178352321592, 2.7814348045817643, 2.2053947247214483, 2.9475396594933923, 7.6965502732190245, 7.741191714387993, 4.4008021800644235, 1.4156880862558818, 3.1536980944675, -11.293563740764009, 8.714017294090489, 9.481018602199223, 4.781171436152855], 'YTD%': [22.855819321112786, -5.941348869649508, 15.117304215482205, 30.914717313288243, 21.15981390085853, 24.597340663914796, 9.157674608158786, 21.039222202640808, 19.065559804985433, 28.082192056143985, 13.69437021949676, 37.73317713704715, 28.56497729371572]})

-----------------------

return 'background-color: %s' % background_color

import numpy as np


def color_negative_red(subset_df):
    # Create Styles based on multiple conditions with np.select
    return np.select(
        [subset_df < 0, subset_df > 0],
        ['background-color: red', 'background-color: green'],
        default=''
    )


# Select Subset of columns
cols = df.columns[df.columns.str.endswith('%')]
# This can also be hard-coded
# cols = ['Daily%', 'WTD%', 'MTD%', 'QTD%', 'YTD%']
(
    df.style.apply(
        color_negative_red, subset=cols, axis=None
    ).format(
        # This can be used to set general number styles without a fmt string
        precision=2, thousands=",", subset='Close'
    ).format(
        # Apply percent format to subset of cols
        '{:,.2f}%', subset=cols
    ).hide_index()  # To remove the "row numbers" hide the index
)

df = pd.DataFrame({'Stock': ['SPY', 'TLT', 'XLY', 'XLF', 'XLV', 'XLK', 'XLP', 'XLI', 'XLB', 'XLE', 'XLU', 'XLRE', 'XLC'], 'Close': [453.0799865722656, 148.17999267578125, 183.52000427246094, 38.06999969482422, 136.85000610351562, 159.36000061035156, 72.83000183105469, 104.58999633789062, 85.37000274658203, 48.619998931884766, 69.48999786376953, 48.72999954223633, 85.47000122070312], 'Daily%': [-0.024275881759957655, -0.9094561130207558, -0.05990339673614984, -0.6264726438619272, 0.10973603410557096, 0.39690348375861295, -0.12341791024475374, -0.6081982311376585, -0.66325305868224, -0.5726024238382843, -0.8277463016708558, 0.0, 0.0], 'WTD%': [0.18795205211694732, -1.1144566965053964, 0.1692033989594774, -0.9882992472694656, 1.078371165114885, -0.2128968969240593, 1.0124873403369272, -0.20038807991586702, -0.5706939905500718, -0.22573488894909668, 1.2678447717357961, 2.8059028977694966, 0.19929444649862676], 'MTD%': [0.28331093711392863, -0.47686662420427856, -0.06534258200082742, -0.28811055388244855, 1.1829989674792052, 0.2894944817725163, 0.26156723981653324, 0.4610467051436526, -0.023418015235201397, 1.9500950554754906, 0.07199801354481171, 0.4742258602810889, -0.6740288326379547], 'QTD%': [5.262178352321592, 2.7814348045817643, 2.2053947247214483, 2.9475396594933923, 7.6965502732190245, 7.741191714387993, 4.4008021800644235, 1.4156880862558818, 3.1536980944675, -11.293563740764009, 8.714017294090489, 9.481018602199223, 4.781171436152855], 'YTD%': [22.855819321112786, -5.941348869649508, 15.117304215482205, 30.914717313288243, 21.15981390085853, 24.597340663914796, 9.157674608158786, 21.039222202640808, 19.065559804985433, 28.082192056143985, 13.69437021949676, 37.73317713704715, 28.56497729371572]})

-----------------------

return 'background-color: %s' % background_color

import numpy as np


def color_negative_red(subset_df):
    # Create Styles based on multiple conditions with np.select
    return np.select(
        [subset_df < 0, subset_df > 0],
        ['background-color: red', 'background-color: green'],
        default=''
    )


# Select Subset of columns
cols = df.columns[df.columns.str.endswith('%')]
# This can also be hard-coded
# cols = ['Daily%', 'WTD%', 'MTD%', 'QTD%', 'YTD%']
(
    df.style.apply(
        color_negative_red, subset=cols, axis=None
    ).format(
        # This can be used to set general number styles without a fmt string
        precision=2, thousands=",", subset='Close'
    ).format(
        # Apply percent format to subset of cols
        '{:,.2f}%', subset=cols
    ).hide_index()  # To remove the "row numbers" hide the index
)

df = pd.DataFrame({'Stock': ['SPY', 'TLT', 'XLY', 'XLF', 'XLV', 'XLK', 'XLP', 'XLI', 'XLB', 'XLE', 'XLU', 'XLRE', 'XLC'], 'Close': [453.0799865722656, 148.17999267578125, 183.52000427246094, 38.06999969482422, 136.85000610351562, 159.36000061035156, 72.83000183105469, 104.58999633789062, 85.37000274658203, 48.619998931884766, 69.48999786376953, 48.72999954223633, 85.47000122070312], 'Daily%': [-0.024275881759957655, -0.9094561130207558, -0.05990339673614984, -0.6264726438619272, 0.10973603410557096, 0.39690348375861295, -0.12341791024475374, -0.6081982311376585, -0.66325305868224, -0.5726024238382843, -0.8277463016708558, 0.0, 0.0], 'WTD%': [0.18795205211694732, -1.1144566965053964, 0.1692033989594774, -0.9882992472694656, 1.078371165114885, -0.2128968969240593, 1.0124873403369272, -0.20038807991586702, -0.5706939905500718, -0.22573488894909668, 1.2678447717357961, 2.8059028977694966, 0.19929444649862676], 'MTD%': [0.28331093711392863, -0.47686662420427856, -0.06534258200082742, -0.28811055388244855, 1.1829989674792052, 0.2894944817725163, 0.26156723981653324, 0.4610467051436526, -0.023418015235201397, 1.9500950554754906, 0.07199801354481171, 0.4742258602810889, -0.6740288326379547], 'QTD%': [5.262178352321592, 2.7814348045817643, 2.2053947247214483, 2.9475396594933923, 7.6965502732190245, 7.741191714387993, 4.4008021800644235, 1.4156880862558818, 3.1536980944675, -11.293563740764009, 8.714017294090489, 9.481018602199223, 4.781171436152855], 'YTD%': [22.855819321112786, -5.941348869649508, 15.117304215482205, 30.914717313288243, 21.15981390085853, 24.597340663914796, 9.157674608158786, 21.039222202640808, 19.065559804985433, 28.082192056143985, 13.69437021949676, 37.73317713704715, 28.56497729371572]})

Remove the requirement to loop through numpy array

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import numpy as np
import pandas as pd

def genMockDataFrame(days,startPrice,colName,startDate,seed=None): 
    ''' identical to the example provided '''

    periods = days*24
    np.random.seed(seed)
    steps = np.random.normal(loc=0, scale=0.0018, size=periods)
    steps[0]=0
    P = startPrice+np.cumsum(steps)
    P = [round(i,4) for i in P]

    fxDF = pd.DataFrame({ 
        'ticker':np.repeat( [colName], periods ),
        'date':np.tile( pd.date_range(startDate, periods=periods, freq='H'), 1 ),
        'price':(P)})
    fxDF.index = pd.to_datetime(fxDF.date)
    fxDF = fxDF.price.resample('D').ohlc()
    fxDF.columns = [i.title() for i in fxDF.columns]
    return fxDF
    
# create the base dataframe according to the former SoFunc
df = genMockDataFrame(10,1.1904,'eurusd','19/3/2020',seed=157)
df["Upper_Band"] = 1.1928
df.loc["2020-03-27"]["Upper_Band"] = 1.2118
df.loc["2020-03-26"]["Upper_Band"] = 1.2200

# create a df out of the cluster array
clusters = np.array([1.1929   , 1.2118 ])
clusters_df = pd.DataFrame({"clust": clusters})

# perform the merge, then filter and finally clean up
result_df = (
    pd
    .merge(df.reset_index(), clusters_df, how="cross") # for each entry in cluster, make a copy of df
    .loc[lambda z: (z.Low <= z.clust) & (z.High >= z.clust) & (z.High >= z.Upper_Band), :] # filter the copies down
    .drop(columns=["clust"]) # not needed in result
    .assign(Level=1.0) # to match your result; not really needed
    .set_index("date") # bring back the old index
)

print(result_df)

See all related Code Snippets

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Confidence Interval 3 dimensional plot
f-string in yaxis.set_major_formatter
Pandas conditional formatting not displaying background colors. (no errors)

Trending Discussions on ticker

QUESTION

How to unlist a list and keep the names of the top level as a new variable in R

Asked 2022-Apr-14 at 12:15

is there a simple way to unlist a list and keep the names of one level as a new variable?

Working example:

# Input list
my_list <- list(Ticker1 = list(date = seq.Date(as.Date('2021-01-01'), as.Date('2021-01-10'), by = 'day'), Value = 1:10),
                Ticker2 = list(date = seq.Date(as.Date('2021-01-01'), as.Date('2021-01-10'), by = 'day'), Value = 11:20),
                Ticker3 = list(date = seq.Date(as.Date('2021-01-01'), as.Date('2021-01-10'), by = 'day'), Value = 21:30))

# Desired data frame
my_df_goal <- as.data.frame(list(Ticker = c(rep("Ticker1", 10), rep("Ticker2", 10), rep("Ticker3", 10)),
                                 date = rep(seq.Date(as.Date('2021-01-01'), as.Date('2021-01-10'), by = 'day'), 3),
                                 Value = 1:30))

There are similar questions regarding unlisting on stackoverflow, but I wasn't able to solve my problem.

One way would probably be

my_df <- as.data.frame(rlist::list.flatten(my_list))

# Same result:
my_df2 <- as.data.frame(unlist(my_list,recursive = F))

#Edit: just noticed that you do not need to unlist first to get the same result:
my_df3 <- as.data.frame(my_list)

and then try to extract the Ticker from the new variable name. But I hope there is an easier solution.

I also tried my_df4 <- reshape2::melt(my_list), but this is not helpful. I have a feeling that the purrr package may have this functionality, but I am not familiar with the package.

I am happy about any help!

Thanks a lot

ANSWER

Answered 2022-Apr-14 at 11:47

Use data.table::rbindlist:

data:table::rbindlist(my_list, idcol = "Ticker")

Or, in the tidyverse:

library(tidyverse)

my_list %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  unnest(cols = c(date, Value))

#  A tibble: 30 x 3
   name    date       Value
   <chr>   <date>     <int>
 1 Ticker1 2021-01-01     1
 2 Ticker1 2021-01-02     2
 3 Ticker1 2021-01-03     3
 4 Ticker1 2021-01-04     4
 5 Ticker1 2021-01-05     5
 6 Ticker1 2021-01-06     6
 7 Ticker1 2021-01-07     7
 8 Ticker1 2021-01-08     8
 9 Ticker1 2021-01-09     9
10 Ticker1 2021-01-10    10
# ... with 20 more rows

Source https://stackoverflow.com/questions/71870968

QUESTION

Error: Property 'body' cannot be accessed on 'Response?' because it is potentially null

Asked 2022-Mar-29 at 00:29

so I'am trying to learn dart and flutter and everything went well so far. But now I'am stuck at an error which I cannot handle. I coded a function which is supposed to asynchronously return the actual BTC price from https://blockchain.info/ticker.

Only thing it returns is errors:

Error: Property 'body' cannot be accessed on 'Response?' because it is potentially null.
 - 'Response' is from 'package:http/src/response.dart' ('/D:/flutter/.pub-cache/hosted/pub.dartlang.org/http-0.13.4/lib/src/response.dart').
Try accessing using ?. instead.
          return Text("${BTCPrice.fromJson(jsonDecode(snapshot.data.body)).eur}");
                                                                    ^^^^
/D:/flutter/packages/flutter/lib/src/widgets/async.dart:242:12: Context: 'data' refers to a property so it couldn't be promoted.
See http://dart.dev/go/non-promo-property
  final T? data;
           ^

My Code:

import 'dart:convert';
import 'package:flutter/cupertino.dart';
import 'package:flutter/material.dart';
import 'package:http/http.dart' as http;

Future<http.Response> fetchBTCPrice() async {
  final response = await http.get(Uri.https('blockhain.info', 'ticker'));
  return response;
}

Widget buildBTCPrice() {
  return FutureBuilder<http.Response>(
    future: fetchBTCPrice(),
    builder: (context, snapshot) {
      if (snapshot.hasData) {
        int? statusCode = snapshot.data?.statusCode;
        if (statusCode == 200) {
          return Text("${BTCPrice.fromJson(jsonDecode(snapshot.data.body)).eur}");
        }
        return Text('$statusCode');

      } else if (snapshot.hasError) {
        return Text('${snapshot.error}');
      }
      return CircularProgressIndicator();
    },
  );
}

class BTCPrice {
  final double eur;
  BTCPrice({required this.eur});

  factory BTCPrice.fromJson(Map<String, dynamic> json) {
    print(json);
    return BTCPrice(
      eur: json['eur']['15m']
    );
  }
}

Last things to mention: I'am running the application on a Android Emulator powerd by Android Studio; and please feel free to hand over any advice you have (in terms of code improvement), even if it is not fixing my issue.

ANSWER

Answered 2022-Mar-29 at 00:29

To get rid of that error you need to use the bang operator to tell the compiler that snapshot.data won't be null.

if (statusCode == 200) {
  return Text(
 "${BTCPrice.fromJson(jsonDecode(snapshot.data!.body)).eur}"); // adding ! on data
}

Edit

The unrelated error you mentioned in your comment:

unexpected character (at character 1) <html><head><title>loading...</title></head><body><script type='text/javasc... ^

would be fixed by changing your GET request from this

final response = await http.get(Uri.https('blockhain.info', 'ticker'));

to this

final response = await http.get(Uri.parse('https://blockchain.info/ticker'));

Source https://stackoverflow.com/questions/71640944

QUESTION

HTML DOM id Array Property & Adding to Stage JavaScript (Array)

Asked 2022-Feb-07 at 14:25

I am trying to create an array of values using a for loop in JavaScript with multiple div id's with successive numbers (i.e. their values) denoting depth. The issue that I am running into is that I can't add the DOM element, i.e. 'text by ship here' successfully to the stage. I added // to show different sections where I am stuck. In particular, I believe I am stuck on the for loop part of the code below //Trying to create a div array here with id's q0, q1, q2, ..., q3 and have used different functions such as for div[0].setAttribute("id", "q0") to be able to connect it with the create.jsDOMElement to add this text to the stage. Any help would be extremely appreciated! My apologies in advance if the code is too long. I have tried to express it succinctly.

<html>
<script type="text/javascript" src="//code.createjs.com/createjs-2013.09.25.combined.js"></script>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<style>
#gameCanvas {
  background-color: lightblue;
}
</style>
<div class="canvasHolder1">
  <div id="ship">⛵ </div> 
<script>
</script>
<canvas id="gameCanvas" width="300" height="300">Not supported</canvas>
<script type="text/javascript">

var stage = new createjs.Stage("gameCanvas");
//Trying to create a div array here with id's q0, q1, q2, ..., q3
   var div = [];
   for(i=0; i<=3; i++){
   div[i] = document.createElement("type");
   div[i].id = `q${i}`;
   div[i].innerHTML = 'text by ship here';
   document.body.appendChild(div[i]);
   }
   //Creating an object array of div elements to add to the stage
  var objectArray = [];
  objectArray[0] = new createjs.DOMElement(document.getElementById("q0"));
  var object = new createjs.DOMElement(document.getElementById("ship"));
  can = document.getElementById("gameCanvas");
  object.x = can.width/3;
  object.y = 50;
  //Adding First Value to the Stage
  objectArray[0].x= can.width/3;
  objectArray[0].y = 55;
  function startGame() {
            stage.addChild(object);
            stage.addChild(objectArray[0]);
           
            createjs.Ticker.addEventListener("tick", handleTick);
              function handleTick(event) {
              drop();
              stage.update();
            }
        }
  function drop(){
  if ( object.y>=can.height){
      object.x += 0;
      object.y +=5;
   }
 }
 
</script>
<body onload="startGame();">
    <div >
  <canvas>This browser or document mode doesn't support canvas object.</canvas>
    </div>
</body>
</html>

I have been able to make some progress on this. I am now running into the issue with the numbers needing to appear relative to the pixel 0, 0 on the Canvas/screen now, i.e. see the word relative.

<h1> I have some text here, and I need the numbers to appear relative to the Canvas/screen. </h1> 
</br> </br>
<script type="text/javascript" src="//code.createjs.com/createjs-2013.09.25.combined.js"></script>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<style>
#gameCanvas {
  background-color: LightCyan;
}
</style>
<div class="canvasHolder1">

  <div id="alien"> |- - - - -| (1.5 mph)</div>
  <div id="alien1"> 🎐</div>
  <div id="alien2"> 🦑 </div>
  <div id="alien3">🦈</div>
  <div id="alien4"> 🐬 </div>
<script>
</script>
<canvas id="gameCanvas" width="300" height="300">Not supported</canvas>
<script type="text/javascript">

  var stage = new createjs.Stage("gameCanvas");
  //Trying to create a div array here with id's q0, q1, q2, ..., q3 k is number of numbers
   var k=10;
   var div = [];
   for(i=0; i<=k; i++){
   div[i] = document.createElement('div');
   div[i].id = `q${i}`;
   div[i].style.position = "relative";
   div[i].innerHTML = i;
   div[i].style.top = 0;
   div[i].style.left = 0;
   document.body.appendChild(div[i]);
   }
     //Creating an object array of div elements to add to the stage
  var objectArray = [];
  for(j=0; j<=k; j++){
  objectArray[j] = new createjs.DOMElement(document.getElementById(`q${j}`));
  }
   var object = new createjs.DOMElement(document.getElementById("alien"));
   var object1 = new createjs.DOMElement(document.getElementById("alien1"));
  var object2 = new createjs.DOMElement(document.getElementById("alien2"));
   var object3 = new createjs.DOMElement(document.getElementById("alien3"));
  var object4 = new createjs.DOMElement(document.getElementById("alien4"));
  can = document.getElementById("gameCanvas");
  for(l=0; l<=k; l++){
  objectArray[l].x= 50;
  objectArray[l].y = can.height/10*l;
  }
  object1.x = can.width/3;
  object1.y = 1;
  object2.x = can.width/5;
  object2.y = 1;
  object3.x = can.width/10;
  object3.y = 1;
  object4.x = can.width/2.5;
  object4.y = 1;
  object.x = can.width/2;
  object.y = 1;
  var speedY=5;
  var speedX=5;
  var speedY1=5;
  var speedX1=5;
  var speedY2=5;
  var speedX2=5;
  var speedY3=5;
  var speedX3=5;
  var speedY4=5;
  var speedX4=5;
  var line = new createjs.Shape();
   stage.addChild(line);
   line.graphics.setStrokeStyle(3).beginStroke("rgba(0,0,0,9)");
   line.graphics.moveTo(30, 0);
   line.graphics.lineTo(35, 0);
   line.graphics.lineTo(30, 0);
   for( let n=0; n<=k; n++){
   var j=5;
   line.graphics.lineTo(30, can.height/10*n);
   line.graphics.lineTo(30+j, can.height/10*n);
   line.graphics.lineTo(30, can.height/10*n);
  }
   line.graphics.endStroke();

        function startGame() {
            stage.addChild(object);
            stage.addChild(object1);
            stage.addChild(object2);
            stage.addChild(object3);
            stage.addChild(object4);
            for(i=0; i<=k; i++){
            stage.addChild(objectArray[i]);
            }
           
            createjs.Ticker.addEventListener("tick", handleTick);
              function handleTick(event) {
              drop();
              bottom();
              right();
              left();
              drop1();
              bottom1();
              right1();
              left1();
              drop2();
              bottom2();
              right2();
              left2();
              drop3();
              bottom3();
              right3();
              left3();
              drop4();
              bottom4();
              right4();
              left4();
              
              stage.update();
            }
        }

  function drop(){
  if ( object.y>=can.height|| speedY==5|| object.y<=0){
      object.x += 0;
      object.y +=1;
      speedY=5;
   }
 }
  function bottom(){
   if (object.y>=can.height|| speedY==-5){  // <----- here
     speedY=5;
     object.y =0.5;
     object.x +=0.5;
     document.getElementById("object").textContent = "3k";
   }
 }
 
   function right(){
   if (object.x>=can.width ||speedY==10  ){  // <----- here
     speedY=10;
     object.y -=1;
     object.x -=1;
   }
 }
 function left(){
   if (0>=object.x ||speedY==30  ){  // <----- here
     speedY=30;
     object.y -=0.5;
     object.x +=0.5;
   }
 }
  function drop1(){
  if ( object1.y>=can.height|| speedY1==5|| object1.y<=0){
      object1.x -= 0.5;
      object1.y +=2;
      speedY1=5;
   }
 }
  function bottom1(){
   if (object1.y>=can.height|| speedY1==-5){  // <----- here
     speedY1=-5;
     object1.y -=2;
     object1.x -=0.5;
   }
 }
 
   function right1(){
   if (object1.x>=can.width ||speedY1==10  ){  // <----- here
     speedY1=10;
     object1.y -=1;
     object1.x -=1.5;
   }
 }
 function left1(){
   if (0>=object1.x ||speedY1==30  ){  // <----- here
     speedY1=30;
     object1.y -=1.5;
     object1.x +=1;
   }
 }
  function drop2(){
  if ( object2.y>=can.height|| speedY2==5|| object2.y<=0){
      object2.x += 0;
      object2.y +=1;
      speedY2=5;
   }
 }
  function bottom2(){
   if (object2.y>=can.height|| speedY2==-5){  // <----- here
     speedY2=-5;
     object2.y -=1;
     object2.x +=5;
   }
 }
 
   function right2(){
   if (object2.x>=can.width ||speedY2==10  ){  // <----- here
     speedY2=10;
     object2.y -=6;
     object2.x -=5;
   }
 }
 function left2(){
   if (0>=object2.x ||speedY2==30  ){  // <----- here
     speedY2=30;
     object2.y -=6;
     object2.x +=5;
   }
 }
  function drop3(){
  if ( object3.y>=can.height|| speedY3==5|| object3.y<=0){
      object3.x += 0.5;
      object3.y +=0.2;
      speedY3=5;
   }
 }
  function bottom3(){
   if (object3.y>=can.height|| speedY3==-5){  // <----- here
     speedY3=-5;
     object3.y -=4;
     object3.x -=2;
   }
 }
   function right3(){
   if (object3.x>=can.width ||speedY3==10  ){  // <----- here
     speedY3=10;
     object3.y -=0.5;
     object3.x -=0.5;
   }
 }
 function left3(){
   if (0>=object3.x ||speedY3==30  ){  // <----- here
     speedY3=30;
     object3.y -=0.5;
     object3.x +=0.5;
   }
 }
  function drop4(){
  if ( object4.y>=can.height|| speedY4==5|| object4.y<=0){
      object4.x += 0;
      object4.y +=0.25;
      speedY4=5;
   }
 }
  function bottom4(){
   if (object4.y>=can.height|| speedY4==-5){  // <----- here
     speedY4=-5;
     object4.y -=0.3;
     object4.x +=1;
   }
 }
   function right4(){
   if (object4.x>=can.width ||speedY4==10  ){  // <----- here
     speedY4=10;
     object4.y -=0.5;
     object4.x -=1;
   }
 }
 function left4(){
   if (0>=object4.x ||speedY4==30  ){  // <----- here
     speedY4=30;
     object4.y -=0.5;
     object4.x +=1;
   }
 }
</script>
<body onload="startGame();">
    <div >
  <canvas>This browser or document mode doesn't support canvas object.</canvas>
    </div>
</body>

ANSWER

Answered 2022-Feb-07 at 14:25

Does this work for you?

<html>
    <script type="text/javascript" src="https://code.createjs.com/1.0.0/createjs.min.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
    <style>
        body { margin: 0; }
        #gameCanvas { background-color: lightblue; }
        .canvasHolder1 { position: relative; }
        .ui { position: absolute; top: 0; left: 0;  pointer-events: none;}
        .ui-element { position: relative; display: inline-block; margin-left: 4px; }
    </style>
    <body onload="startGame();">
        <div class="canvasHolder1">
            <div id="ship">⛵ </div>
            <div class="ui"></div>
            <canvas id="gameCanvas" width="300" height="300">Not supported</canvas>
        </div>

        <script type="text/javascript">
            const stage = new createjs.Stage("gameCanvas");
            const ship = document.getElementById("ship");
            const ui = document.querySelector(".ui");
            const canvas = document.getElementById("gameCanvas");
            const canvasHolder = document.querySelector('.canvasHolder1');

            var divs = [];
            const texts = [ 'My Ship', 'Pos X', 'Pos Y', 'Other' ];

            for (i = 0; i <= 3; i++) {
                const newDiv = document.createElement("type");
                newDiv.id = `q${i}`;
                newDiv.className = `ui-element`;
                newDiv.innerHTML = texts[i];
                ui.appendChild(newDiv);
                divs.push(newDiv);
            }


            const textXPosition = divs[1];
            const textYPosition = divs[2];
            const textProgress = divs[3];

            canvas.addEventListener('mousemove', (evt) => {
                textXPosition.innerHTML = evt.pageX;
                textYPosition.innerHTML = evt.pageY;
            });


            var objectArray = [];
            objectArray.push(new createjs.DOMElement(divs[0]));
            const myFirstText = objectArray[0];
            const myFirstTextWidth = parseInt(myFirstText.htmlElement.offsetWidth);

            var shipJS = new createjs.DOMElement(ship);

            function applyPosition(posObject) {
                shipJS.x = posObject.x;
                shipJS.y = posObject.y;

                myFirstText.x = posObject.x - (myFirstTextWidth / 2);
                myFirstText.y = posObject.y - 15;
            }

            const shipPosition = {
                x: canvas.width / 3,
                y: 50
            };
            applyPosition(shipPosition);


            let looperCount = -1;
            const step = 0.02;
            const distance = canvas.width / 3;

            function handleTick(event) {
                if (looperCount >= 1) { looperCount = -1; }
                looperCount += step;
                const progress = Math.PI * looperCount;
                textProgress.innerHTML = parseInt(((looperCount + 1) / 2) * 100 )+'%';

                applyPosition({
                    x: (Math.cos(progress) * distance) + (distance * 1.5),
                    y: (Math.sin(progress) * distance) + (distance * 1.5)
                });
                stage.update();
            }


            function startGame() {
                stage.addChild(shipJS);
                stage.addChild(myFirstText);

                createjs.Ticker.addEventListener("tick", handleTick);

            }
        </script>
    </body>
</html>

Source https://stackoverflow.com/questions/71010771

QUESTION

Vectorize for loop and return x day high and low

Asked 2021-Dec-01 at 22:16

Overview

For each row of a dataframe I want to calculate the x day high and low.

An x day high is higher than previous x days. An x day low is lower than previous x days.

The for loop is explained in further detail in this post

Update:

Answer by @mozway below completes in around 20 seconds with dataset containing 18k rows. Can this be improved with numpy with broadcasting etc?

Example

2020-03-20 has an x_day_low value of 1 as it is lower than the previous day.

2020-03-27 has an x_day_high value of 8 as it is higher than the previous 8 days.

See desired output and test code below which is calculated with a for loop in the findHighLow function. How would I vectorize findHighLow as the actual dataframe is somewhat larger.

Test data

def genMockDataFrame(days,startPrice,colName,startDate,seed=None): 
   
    periods = days*24
    np.random.seed(seed)
    steps = np.random.normal(loc=0, scale=0.0018, size=periods)
    steps[0]=0
    P = startPrice+np.cumsum(steps)
    P = [round(i,4) for i in P]

    fxDF = pd.DataFrame({ 
        'ticker':np.repeat( [colName], periods ),
        'date':np.tile( pd.date_range(startDate, periods=periods, freq='H'), 1 ),
        'price':(P)})
    fxDF.index = pd.to_datetime(fxDF.date)
    fxDF = fxDF.price.resample('D').ohlc()
    fxDF.columns = [i.title() for i in fxDF.columns]
    return fxDF

#rows set to 15 for minimal example but actual dataframe contains around 18000 rows.
number_of_rows = 15    

df = genMockDataFrame(number_of_rows,1.1904,'tttmmm','19/3/2020',seed=157)

def findHighLow (df):

    df['x_day_high'] = 0
    df['x_day_low'] = 0

    for n in reversed(range(len(df['High']))):
        for i in reversed(range(n)):
                if df['High'][n] > df['High'][i]:
                    df['x_day_high'][n] = n - i
                else: break

    for n in reversed(range(len(df['Low']))):
        for i in reversed(range(n)):
                if df['Low'][n] < df['Low'][i]:
                    df['x_day_low'][n] = n - i
                else: break
    return df

df = findHighLow (df)

Desired output should match this:

df[["High","Low","x_day_high","x_day_low"]]

             High   Low x_day_high  x_day_low
date                
2020-03-19  1.1937  1.1832  0       0
2020-03-20  1.1879  1.1769  0       1
2020-03-21  1.1767  1.1662  0       2
2020-03-22  1.1721  1.1611  0       3
2020-03-23  1.1819  1.1690  2       0
2020-03-24  1.1928  1.1807  4       0
2020-03-25  1.1939  1.1864  6       0
2020-03-26  1.2141  1.1964  7       0
2020-03-27  1.2144  1.2039  8       0
2020-03-28  1.2099  1.2018  0       1
2020-03-29  1.2033  1.1853  0       4
2020-03-30  1.1887  1.1806  0       6
2020-03-31  1.1972  1.1873  1       0 
2020-04-01  1.1997  1.1914  2       0
2020-04-02  1.1924  1.1781  0       9

ANSWER

Answered 2021-Nov-28 at 11:30

You can use rolling to get the last N days, a comparison + cumprod on the reversed boolean array to keep only the last consecutive valid values, and sum to count them. Apply on each column using agg and join the output after adding a prefix.

# number of days
N = 8

df.join(df.rolling(f'{N+1}d', min_periods=1)
          .agg({'High': lambda s: s.le(s.iloc[-1])[::-1].cumprod().sum()-1,
                'Low': lambda s: s.ge(s.iloc[-1])[::-1].cumprod().sum()-1,
               })
          .add_prefix(f'{N}_days_')
        )

Output:

              Open    High     Low   Close  8_days_High  8_days_Low
date                                                               
2020-03-19  1.1904  1.1937  1.1832  1.1832          0.0         0.0
2020-03-20  1.1843  1.1879  1.1769  1.1772          0.0         1.0
2020-03-21  1.1755  1.1767  1.1662  1.1672          0.0         2.0
2020-03-22  1.1686  1.1721  1.1611  1.1721          0.0         3.0
2020-03-23  1.1732  1.1819  1.1690  1.1819          2.0         0.0
2020-03-24  1.1836  1.1928  1.1807  1.1922          4.0         0.0
2020-03-25  1.1939  1.1939  1.1864  1.1936          6.0         0.0
2020-03-26  1.1967  1.2141  1.1964  1.2114          7.0         0.0
2020-03-27  1.2118  1.2144  1.2039  1.2089          7.0         0.0
2020-03-28  1.2080  1.2099  1.2018  1.2041          0.0         1.0
2020-03-29  1.2033  1.2033  1.1853  1.1880          0.0         4.0
2020-03-30  1.1876  1.1887  1.1806  1.1879          0.0         6.0
2020-03-31  1.1921  1.1972  1.1873  1.1939          1.0         0.0
2020-04-01  1.1932  1.1997  1.1914  1.1914          2.0         0.0
2020-04-02  1.1902  1.1924  1.1781  1.1862          0.0         7.0

Source https://stackoverflow.com/questions/70141716

QUESTION

How to make labels appear when clicking on a step plot subplot point in matplotlib (or possibly plotly)?

Asked 2021-Nov-06 at 10:09

I'm using matplotlib to make step graphs based on a dataframe, but I want one of the key/value of the dataframe to appear (signals_df['Gage']), instead of coordinates as annotation, but I always get the error: AttributeError: 'Line2D' object has no attribute 'get_offsets' when I click on the first subplot from bottom to top and the annotation does not appear. In fact, I commented out the annot.set_visible(False)and replaced the "" of the examples with val_gage, so that it will look like I want the annotation to appear one by one, when clicking on some point within the subplots. This is the code in question:

import pandas as pd
import numpy as np
import matplotlib as mtpl
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker

annot = mtpl.text.Annotation

data = {
    # 'Name': ['Status', 'Status', 'HMI', 'Allst', 'Drvr', 'CurrTUBand', 'RUSource', 'RUReqstrPriority', 'RUReqstrSystem', 'RUResReqstStat', 'CurrTUBand', 'DSP', 'SetDSP', 'SetDSP', 'DSP', 'RUSource', 'RUReqstrPriority', 'RUReqstrSystem', 'RUResReqstStat', 'Status', 'Delay', 'Status', 'Delay', 'HMI', 'Status', 'Status', 'HMI', 'DSP'],
    # 'Value': [4, 4, 2, 1, 1, 1, 0, 7, 0, 4, 1, 1, 3, 0, 3, 0, 7, 0, 4, 1, 0, 1, 0, 1, 4, 4, 2, 3],
    # 'Gage': ['H1', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H1', 'H1', 'H3', 'H3', 'H3', 'H1', 'H3', 'H3', 'H3'],
    # 'Id_Par': [0, 0, 0, 0, 0, 0, 10, 10, 10, 10, 10, 0, 0, 22, 22, 28, 28, 28, 28, 0, 0, 38, 38, 0, 0, 0, 0, 0]
    'Name': ['Lamp_D_Rq', 'Status', 'Status', 'HMI', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lamp_D_Rq', 'Lamp_D_Rq', 'Lamp_D_Rq', 'Lamp_D_Rq'],
    'Value': [0, 4, 4, 2, 1, 1, 2, 2, 1, 1, 3, 3],
    'Gage': ['F1', 'H1', 'H3', 'H3', 'H3', 'F1', 'H3', 'F1', 'F1', 'H3', 'F1', 'H3'],
    'Id_Par': [0, 0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0]
    }

signals_df = pd.DataFrame(data)


def plot_signals(signals_df):
    print(signals_df)
    # Count signals by parallel
    signals_df['Count'] = signals_df.groupby('Id_Par').cumcount().add(1).mask(signals_df['Id_Par'].eq(0), 0)
    # Subtract Parallel values from the index column
    signals_df['Sub'] = signals_df.index - signals_df['Count']
    id_par_prev = signals_df['Id_Par'].unique()
    id_par = np.delete(id_par_prev, 0)
    signals_df['Prev'] = [1 if x in id_par else 0 for x in signals_df['Id_Par']]
    signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
    # Convert and set Subtract to index
    signals_df.set_index('Final', inplace=True)

    # Get individual names and variables for the chart
    names_list = [name for name in signals_df['Name'].unique()]
    num_names_list = len(names_list)
    num_axisx = len(signals_df["Name"])

    # Matplotlib's categorical feature to convert x-axis values to string
    x_values = [-1, ]
    x_values += (list(set(signals_df.index)))
    x_values = [str(i) for i in sorted(x_values)]

    # Creation Graphics
    fig, ax = plt.subplots(nrows=num_names_list, figsize=(10, 10), sharex=True)
    plt.xticks(np.arange(0, num_axisx), color='SteelBlue', fontweight='bold')

    # Loop to build the different graphs
    for pos, name in enumerate(names_list):
        # Creating a dummy plot and then remove it
        dummy, = ax[pos].plot(x_values, np.zeros_like(x_values))
        dummy.remove()

        # Get names by values and gage data
        data = signals_df[signals_df["Name"] == name]["Value"]
        data_gage = signals_df[signals_df["Name"] == name]["Gage"]

        # Get values axis-x and axis-y
        x_ = np.hstack([-1, data.index.values, len(signals_df) - 1])
        y_ = np.hstack([0, data.values, data.iloc[-1]])
        y_gage = np.hstack(["", "-", data_gage.values])
        # print(y_gage)

        # Plotting the data by position
        steps = ax[pos].plot(x_.astype('str'), y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
        ax[pos].set_ylabel(name, fontsize=8, fontweight='bold', color='SteelBlue', rotation=30, labelpad=35)
        ax[pos].yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
        ax[pos].yaxis.set_tick_params(labelsize=6)
        ax[pos].grid(alpha=0.4, color='SteelBlue')
        # Labeling the markers with Values and Gage
        xy_temp = []
        for i in range(len(y_)):
            if i == 0:
                xy = [x_[0].astype('str'), y_[0]]
                xy_temp.append(xy)
            else:
                xy = [x_[i - 1].astype('str'), y_[i - 1]]
                xy_temp.append(xy)

            # Creating values in text inside the plot
            ax[pos].text(x=xy[0], y=xy[1], s=str(xy[1]), color='k', fontweight='bold', fontsize=12)

            for val_gage, xy in zip(y_gage, xy_temp):
                annot = ax[pos].annotate(val_gage, xy=xy, xytext=(-20, 20), textcoords="offset points",
                                         bbox=dict(boxstyle="round", fc="w"),
                                         arrowprops=dict(arrowstyle="->"))
                # annot.set_visible(False)

    # Function for storing and showing the clicked values
    def update_annot(ind):
        print("Enter update_annot")
        coord = steps[0].get_offsets()[ind["ind"][0]]
        annot.xy = coord
        text = "{}, {}".format(" ".join(list(map(str, ind["ind"]))),
                                " ".join([y_gage[n] for n in ind["ind"]]))
        annot.set_text(text)
        annot.get_bbox_patch().set_alpha(0.4)

    def on_click(event):
        print("Enter on_click")
        vis = annot.get_visible()
        # print(event.inaxes)
        # print(ax[pos])
        # print(event.inaxes == ax[pos])
        if event.inaxes == ax[pos]:
            cont, ind = steps[0].contains(event)
            if cont:
                update_annot(ind)
                annot.set_visible(True)
                fig.canvas.draw_idle()
            else:
                if vis:
                    annot.set_visible(False)
                    fig.canvas.draw_idle()

    fig.canvas.mpl_connect("button_press_event",on_click)

    plt.show()

plot_signals(signals_df)

I've tested and reviewed many answers and code like the following:

I even reviewed the mplcursors module for a long time, since it comes with an example with a graph of steps similar to what I'm doing: https://mplcursors.readthedocs.io/en/stable/examples/step.html, but it gives me the same result and I can't find the solution.

ANSWER

Answered 2021-Nov-04 at 11:07

Without knowing much about the libraries you are using I can see you are creating these annotation objects and then assigning them to a global variable that is re-assigned later and thus you lose the right object to make it visible.

Instead you could keep the annotation objects into a dictionary and try to retrieve them later when you need them based on an object.

I used a list to show you the idea, but you need a dictionary I guess to identify the right objects.

I modified your code a bit and it shows the desired behaviour if you resize the window...I guess you have to find a way to refresh the plot also:

import pandas as pd
import numpy as np
import matplotlib as mtpl
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker

annotations = []
data = {
    # 'Name': ['Status', 'Status', 'HMI', 'Allst', 'Drvr', 'CurrTUBand', 'RUSource', 'RUReqstrPriority', 'RUReqstrSystem', 'RUResReqstStat', 'CurrTUBand', 'DSP', 'SetDSP', 'SetDSP', 'DSP', 'RUSource', 'RUReqstrPriority', 'RUReqstrSystem', 'RUResReqstStat', 'Status', 'Delay', 'Status', 'Delay', 'HMI', 'Status', 'Status', 'HMI', 'DSP'],
    # 'Value': [4, 4, 2, 1, 1, 1, 0, 7, 0, 4, 1, 1, 3, 0, 3, 0, 7, 0, 4, 1, 0, 1, 0, 1, 4, 4, 2, 3],
    # 'Gage': ['H1', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H3', 'H1', 'H1', 'H3', 'H3', 'H3', 'H1', 'H3', 'H3', 'H3'],
    # 'Id_Par': [0, 0, 0, 0, 0, 0, 10, 10, 10, 10, 10, 0, 0, 22, 22, 28, 28, 28, 28, 0, 0, 38, 38, 0, 0, 0, 0, 0]
    'Name': ['Lamp_D_Rq', 'Status', 'Status', 'HMI', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lck_D_RqDrv3', 'Lamp_D_Rq', 'Lamp_D_Rq', 'Lamp_D_Rq', 'Lamp_D_Rq'],
    'Value': [0, 4, 4, 2, 1, 1, 2, 2, 1, 1, 3, 3],
    'Gage': ['F1', 'H1', 'H3', 'H3', 'H3', 'F1', 'H3', 'F1', 'F1', 'H3', 'F1', 'H3'],
    'Id_Par': [0, 0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0]
    }

signals_df = pd.DataFrame(data)


def plot_signals(signals_df):
    print(signals_df)
    # Count signals by parallel
    signals_df['Count'] = signals_df.groupby('Id_Par').cumcount().add(1).mask(signals_df['Id_Par'].eq(0), 0)
    # Subtract Parallel values from the index column
    signals_df['Sub'] = signals_df.index - signals_df['Count']
    id_par_prev = signals_df['Id_Par'].unique()
    id_par = np.delete(id_par_prev, 0)
    signals_df['Prev'] = [1 if x in id_par else 0 for x in signals_df['Id_Par']]
    signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
    # Convert and set Subtract to index
    signals_df.set_index('Final', inplace=True)

    # Get individual names and variables for the chart
    names_list = [name for name in signals_df['Name'].unique()]
    num_names_list = len(names_list)
    num_axisx = len(signals_df["Name"])

    # Matplotlib's categorical feature to convert x-axis values to string
    x_values = [-1, ]
    x_values += (list(set(signals_df.index)))
    x_values = [str(i) for i in sorted(x_values)]

    # Creation Graphics
    fig, ax = plt.subplots(nrows=num_names_list, figsize=(10, 10), sharex=True)
    plt.xticks(np.arange(0, num_axisx), color='SteelBlue', fontweight='bold')

    # Loop to build the different graphs
    for pos, name in enumerate(names_list):
        print("name: %s" % name)
        print("pos: %s" % pos)
        # Creating a dummy plot and then remove it
        dummy, = ax[pos].plot(x_values, np.zeros_like(x_values))
        dummy.remove()

        # Get names by values and gage data
        data = signals_df[signals_df["Name"] == name]["Value"]
        data_gage = signals_df[signals_df["Name"] == name]["Gage"]

        # Get values axis-x and axis-y
        x_ = np.hstack([-1, data.index.values, len(signals_df) - 1])
        y_ = np.hstack([0, data.values, data.iloc[-1]])
        y_gage = np.hstack(["", "-", data_gage.values])
        # print(y_gage)

        # Plotting the data by position
        steps = ax[pos].plot(x_.astype('str'), y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
        ax[pos].set_ylabel(name, fontsize=8, fontweight='bold', color='SteelBlue', rotation=30, labelpad=35)
        ax[pos].yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
        ax[pos].yaxis.set_tick_params(labelsize=6)
        ax[pos].grid(alpha=0.4, color='SteelBlue')
        # Labeling the markers with Values and Gage
        xy_temp = []
        for i in range(len(y_)):
            if i == 0:
                xy = [x_[0].astype('str'), y_[0]]
                xy_temp.append(xy)
            else:
                xy = [x_[i - 1].astype('str'), y_[i - 1]]
                xy_temp.append(xy)

            # Creating values in text inside the plot
            ax[pos].text(x=xy[0], y=xy[1], s=str(xy[1]), color='k', fontweight='bold', fontsize=12)

            for val_gage, xy in zip(y_gage, xy_temp):
                print("val_gage: %s" % val_gage)
                annot = ax[pos].annotate(val_gage, xy=xy, xytext=(-20, 20), textcoords="offset points",
                                         bbox=dict(boxstyle="round", fc="w"),
                                         arrowprops=dict(arrowstyle="->"))

                annot.set_visible(False)
                annotations.append(annot)

    # Function for storing and showing the clicked values
    def update_annot(ind):
        print("Enter update_annot")
        coord = steps[0].get_offsets()[ind["ind"][0]]
        annot.xy = coord
        text = "{}, {}".format(" ".join(list(map(str, ind["ind"]))),
                                " ".join([y_gage[n] for n in ind["ind"]]))
        annot.set_text(text)
        annot.get_bbox_patch().set_alpha(0.4)

    def on_click(event):
        print("Enter on_click")
        vis = annot.get_visible()
        # make the first three annotations visible
        for i in range(0, 3):
            print('elem visible')
            annotations[i].set_visible(True)
        print(event.inaxes)
        print(ax[pos])
        print(event.inaxes == ax[pos])
        if event.inaxes == ax[pos]:
            cont, ind = steps[0].contains(event)
            print (ind)
            if cont:
                update_annot(ind)
                annot.set_visible(True)
                fig.canvas.draw_idle()
            else:
                if vis:
                    annot.set_visible(False)
                    fig.canvas.draw_idle()

    fig.canvas.mpl_connect("button_press_event",on_click)

    plt.show()

plot_signals(signals_df)

I hope this helps and it will solve your problem. It looks more like a python/programming problem and not so much related to the libraries you are using if I get this right :)

Source https://stackoverflow.com/questions/69730420

QUESTION

React prevent re-render by using functional variant of useState

Asked 2021-Oct-24 at 08:55

I am trying to understand the exact difference in terms of how the re-render of function component is caused in one case using plain setState V/s other case which uses functional state update

The relevant code snippet is as below

Case 1 : Causes re-render of the component

const onRemove = useCallback(
  tickerToRemove => {
    setWatchlist(watchlist.filter(ticker => ticker !== tickerToRemove));
  },
  [watchlist]
);

Case 2 : Does not cause re-render

const onRemove = useCallback(tickerToRemove => {
  setWatchlist(watchlist =>
    watchlist.filter(ticker => ticker !== tickerToRemove)
  );
}, []);

Full example of both the use-cases can be seen on;

https://codesandbox.io/s/06c-usecallback-final-no-rerenders-bsm64?file=/src/watchlistComponent.js

https://codesandbox.io/s/06b-usecallback-usememo-ngwev?file=/src/watchlistComponent.js:961-970

UPDATE

Full article link https://medium.com/@guptagaruda/react-hooks-understanding-component-re-renders-9708ddee9928#204b

I am a bit confused as to how the re-render of child components is prevented.

In the article it says

"Thankfully, setter function from useState hook supports a functional variant which comes to our rescue. Instead of calling setWatchlist with the updated watchlist array, we can instead send a function that gets the current state as an argument"

However, I am a bit confused whether the re-rendering of child components is prevented because we use empty array (as [] does not changes between renders) V/s prevented because of using setter variant of useState hook ?

ANSWER

Answered 2021-Oct-24 at 07:39

Using a functional state update or not is rather irrelevant to the question you are asking about. You appear to be asking why (1) a callback with dependency triggers a rerender versus (2) a callback with empty dependency.

The answer is quite literally very simple. In version (2) you are providing a stable callback reference from the time the component mounts that never changes, whereas in (1) the callback reference changes when the dependency does. Remember that React components rerender when state or props update (a new callback reference is a new prop reference) or when the parent component rerenders. Since the onRemove prop is updating in (1) it triggers a rerender.

Source https://stackoverflow.com/questions/69694194

QUESTION

Confidence Interval 3 dimensional plot

Asked 2021-Oct-14 at 14:53

I have a 3-dimensional plot and I am able to plot it with the code written below.

Considering that my point distribution is represented by a 100x100 matrix, is it possible to plot a confidence interval on my data? In the code below, my data are called "result", while the upper bound and lower bound that I want to show are called "upper_bound" and "lower_bound".

For example, I am asking if exist something like this, but in 3 dimension (instead of 2 dimension like the picture below)

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter

interval = np.random.normal(0, 1, size=(100, 100))

x = np.arange(0.1,1.1,0.01)
y = np.linspace(-np.pi,np.pi,100)

X,Y = np.meshgrid(x,y)
result = []
for i,j in zip(X,Y):
    result.append(np.log(i)+np.sin(j))

upper_bound = np.array(result)+interval
lower_bound = np.array(result)-interval

fig = plt.figure()
fig.set_figwidth(20)
fig.set_figheight(6)
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, np.array(result))
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()

ANSWER

Answered 2021-Oct-14 at 14:53

Check out this 3d surface plot using plotly graph objects:

import plotly.graph_objects as go
import numpy as np

x = np.arange(0.1,1.1,0.01)
y = np.linspace(-np.pi,np.pi,100)

X,Y = np.meshgrid(x,y)
result = []
for i,j in zip(X,Y):
    result.append(np.log(i)+np.sin(j))

upper_bound = np.array(result)+1
lower_bound = np.array(result)-1

fig = go.Figure(data=[
    go.Surface(z=result),
    go.Surface(z=upper_bound, showscale=False, opacity=0.3,colorscale='purp'),
    go.Surface(z=lower_bound, showscale=False, opacity=0.3,colorscale='purp'),

])
fig.show()

This plots 3 surfaces, the one for your results and the 2 bounds. However if you'd like something that looks more like a filled volume you'd have to add volume graphs with scaling opacity.

Source https://stackoverflow.com/questions/69526947

QUESTION

f-string in yaxis.set_major_formatter

Asked 2021-Sep-05 at 19:58

I have the following code:

import pandas as pd
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
import yfinance as yf
import matplotlib.ticker as mtick
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
                               AutoMinorLocator)
import currency
import warnings

warnings.filterwarnings("ignore")

start = datetime.date(2000,1,1)
end = datetime.date.today()


stock =  'goog'
fig, ax = plt.subplots(dpi=300, figsize =(8,4) )
data = web.DataReader(stock, 'yahoo', start, end)
data['Close'].plot()
ax.tick_params(axis='y', colors='midnightblue')

ax.tick_params(axis='x', colors="k")
pg = yf.Ticker(stock)
# sn = pg.info['shoName']
sn = pg.info['shortName']

b = pg.info['currency']
c = currency.symbol(f"{b}")
ax.set_ylabel(f"Price ({pg.info['currency']})")
ax.xaxis.grid(False, which='minor')
ax.yaxis.set_major_formatter('${x:1.2f}')
ax.margins(x=0)
# plt.savefig(f"{sn} {end.strftime('%d - %b%Y')}", bbox_inches='tight', dpi = 500)
print(sn)
print('datetime date =', start)
plt.show()
print()

The issue i am facing is ax.yaxis.set_major_formatter('${x:1.2f}'). I need to get a f-string to evaluate c. This would give the currency of any country instead of using $ sign. However it does not seem to evaluate f-string, could you please advise any possible alternatives?

ANSWER

Answered 2021-Sep-05 at 19:58

The tick string formatter expects a format string with x (and optionally pos):

The field used for the tick value must be labeled x and the field used for the tick position must be labeled pos.

That means we need to evaluate c but not x, so:

Either concatenate c with the format string:

ax.yaxis.set_major_formatter(c + '{x:1.2f}')

Or pass an evaluated f-string (add f) where x's braces are escaped (single braces for c, double braces for x):

ax.yaxis.set_major_formatter(f'{c}{{x:1.2f}}')

Source https://stackoverflow.com/questions/69065509

QUESTION

Pandas conditional formatting not displaying background colors. (no errors)

Asked 2021-Sep-05 at 00:01

I am using a jupyter notebook for this project. I am trying to add conditional formatting to my data frame. I would like to give the negative numbers a red background and the positive numbers a green background and if possible get rid of the row numbers. The code I am trying to use down at the bottom does not give back any errors.

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import pandas_datareader as data

tickers = ['SPY', 'TLT', 'XLY', 'XLF', 'XLV', 'XLK', 'XLP', 'XLI', 'XLB', 'XLE', 'XLU', 'XLRE', 'XLC']
df_list = []
for ticker in tickers:
    prices = data.DataReader(ticker, 'yahoo', '2021')['Close']

    # get all timestamps for specific lookups
    today = prices.index[-1]
    yest= prices.index[-2]
    start = prices.index[0]
    week = today - pd.tseries.offsets.Week(weekday=0)
    month = today - pd.tseries.offsets.BMonthBegin()
    quarter = today - pd.tseries.offsets.BQuarterBegin(startingMonth=1)

    # calculate percentage changes
    close = prices[today]
    daily =  (close - prices[yest]) / prices[yest]*100
    wtd = (close - prices[week]) / prices[week]*100
    mtd = (close - prices[month]) / prices[month]*100
    qtd = (close - prices[quarter]) / prices[quarter]*100
    ytd = (close - prices[start]) / prices[start]*100

    # create temporary frame for current ticker
    df = pd.DataFrame(data=[[ticker, close, daily, wtd, mtd, qtd, ytd]],
                      columns=['Stock', 'Close', 'Daily%', 'WTD%', 'MTD%', 'QTD%', 'YTD%'])
    df_list.append(df)



# stack all frames
df = pd.concat(df_list, ignore_index=True)

#conditional formatting highlight negative numbers red background and positive numbers green background in return data



def color_negative_red(value):
  """
  Colors elements in a dateframe
  green if positive and red if
  negative. Does not color NaN
  values.
  """

  if value < 0:
    background_color = 'red'
  elif value > 0:
    background_color = 'green'
  else:
    background_color = ''

  return 'background_color: %s' % background_color

df.style.applymap(color_negative_red, subset=['Daily%', 'WTD%', 'MTD%', 'QTD%', 'YTD%']).format({
    'Close': '{:,.2f}'.format,
    'Daily%': '{:,.2f}%'.format,
    'WTD%': '{:,.2f}%'.format,
    'MTD%': '{:,.2f}%'.format,
    'QTD%': '{:,.2f}%'.format,
    'YTD%': '{:,.2f}%'.format,
})

output:

desired output something like this with red and green instead of yellow:

ANSWER

Answered 2021-Sep-05 at 00:01

In some regards this specific issue with the styling is simple. The CSS property is background-color not background_color.

The only change strictly necessary is to get the styling working is:

return 'background-color: %s' % background_color

However, in regards to the general approach, as well as the question of how to remove the "row numbers", there are a few changes to be made.

Columns can be programmatically filtered with something like str.endswith instead of manually typing out the columns. Also a variable can be helpful when re-using the same column subset.
It is more common to create a 2d structure representing all the styles with something like np.select which accepts a list of conditions and a list of outputs.
The Styler.format can be subset. So instead of lots of copies of the same format string in a dictionary take advantage of this.
The Styler.hide_index method can be used to remove the "row numbers", also known as the DataFrame index.

import numpy as np


def color_negative_red(subset_df):
    # Create Styles based on multiple conditions with np.select
    return np.select(
        [subset_df < 0, subset_df > 0],
        ['background-color: red', 'background-color: green'],
        default=''
    )


# Select Subset of columns
cols = df.columns[df.columns.str.endswith('%')]
# This can also be hard-coded
# cols = ['Daily%', 'WTD%', 'MTD%', 'QTD%', 'YTD%']
(
    df.style.apply(
        color_negative_red, subset=cols, axis=None
    ).format(
        # This can be used to set general number styles without a fmt string
        precision=2, thousands=",", subset='Close'
    ).format(
        # Apply percent format to subset of cols
        '{:,.2f}%', subset=cols
    ).hide_index()  # To remove the "row numbers" hide the index
)

Sample DataFrame used:

df = pd.DataFrame({'Stock': ['SPY', 'TLT', 'XLY', 'XLF', 'XLV', 'XLK', 'XLP', 'XLI', 'XLB', 'XLE', 'XLU', 'XLRE', 'XLC'], 'Close': [453.0799865722656, 148.17999267578125, 183.52000427246094, 38.06999969482422, 136.85000610351562, 159.36000061035156, 72.83000183105469, 104.58999633789062, 85.37000274658203, 48.619998931884766, 69.48999786376953, 48.72999954223633, 85.47000122070312], 'Daily%': [-0.024275881759957655, -0.9094561130207558, -0.05990339673614984, -0.6264726438619272, 0.10973603410557096, 0.39690348375861295, -0.12341791024475374, -0.6081982311376585, -0.66325305868224, -0.5726024238382843, -0.8277463016708558, 0.0, 0.0], 'WTD%': [0.18795205211694732, -1.1144566965053964, 0.1692033989594774, -0.9882992472694656, 1.078371165114885, -0.2128968969240593, 1.0124873403369272, -0.20038807991586702, -0.5706939905500718, -0.22573488894909668, 1.2678447717357961, 2.8059028977694966, 0.19929444649862676], 'MTD%': [0.28331093711392863, -0.47686662420427856, -0.06534258200082742, -0.28811055388244855, 1.1829989674792052, 0.2894944817725163, 0.26156723981653324, 0.4610467051436526, -0.023418015235201397, 1.9500950554754906, 0.07199801354481171, 0.4742258602810889, -0.6740288326379547], 'QTD%': [5.262178352321592, 2.7814348045817643, 2.2053947247214483, 2.9475396594933923, 7.6965502732190245, 7.741191714387993, 4.4008021800644235, 1.4156880862558818, 3.1536980944675, -11.293563740764009, 8.714017294090489, 9.481018602199223, 4.781171436152855], 'YTD%': [22.855819321112786, -5.941348869649508, 15.117304215482205, 30.914717313288243, 21.15981390085853, 24.597340663914796, 9.157674608158786, 21.039222202640808, 19.065559804985433, 28.082192056143985, 13.69437021949676, 37.73317713704715, 28.56497729371572]})

Source https://stackoverflow.com/questions/69059271

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Vulnerabilities

No vulnerabilities reported

Install ticker

Add the ticker dependency to your build.gradle.

Support

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